commit e72af82c3ed16b81cdb5043c58abbdbb3cf02102 Author: Ian Jauslin Date: Mon Oct 4 11:12:34 2021 -0400 Initial commit diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..d645695 --- /dev/null +++ b/LICENSE @@ -0,0 +1,202 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. Definitions. + + "License" shall mean the terms and conditions for use, reproduction, + and distribution as defined by Sections 1 through 9 of this document. + + "Licensor" shall mean the copyright owner or entity authorized by + the copyright owner that is granting the License. + + "Legal Entity" shall mean the union of the acting entity and all + other entities that control, are controlled by, or are under common + control with that entity. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/NOTICE b/NOTICE new file mode 100644 index 0000000..216093f --- /dev/null +++ b/NOTICE @@ -0,0 +1,4 @@ +simplesolv: +Licensed under the Apache 2.0 License (see LICENSE for details) +Copyright Ian Jauslin 2021 + diff --git a/README b/README new file mode 100644 index 0000000..ffc52d5 --- /dev/null +++ b/README @@ -0,0 +1,102 @@ +simplesolv is a program to compute the solution of the Simplified approach to +the Bose gas. + +It is written in julia, and requires the julia interpreter to run. + +The code is available in the 'src' directory. + +The LaTeX source code for the documentation is located in the 'doc' directory. +A pdf file is available in the source tarball, as well as at + http://ian.jauslin.org/software/simplesolv/simplesolv-doc.pdf +The documentation basic usage examples, an extensive reference of all commands, +and detailed descriptions of the numerical algorithms used. + +Any questions, concerns, or bug reports should be addressed to Ian Jauslin at + ian.jauslin@rutgers.edu + +simplesolv was written by Ian Jauslin, and is released under the Apache 2.0 +license. It is based on work done in collaboration with + Eric A. Carlen + Markus Holzmann + Elliott H. Lieb +published in the following papers: + * E.A. Carlen, I. Jauslin, E.H. Lieb - Analysis of a simple equation for the + ground state energy of the Bose gas, Pure and Applied Analysis, volume 2, + issue 3, pages 659-684, 2020, + https://doi.org/10.2140/paa.2020.2.659 + https://arxiv.org/abs/1912.04987 + http://ian.jauslin.org/publications/19cjl + + * E.A. Carlen, I. Jauslin, E.H. Lieb - Analysis of a simple equation for the + ground state energy of the Bose gas II: Monotonicity, Convexity and + Condensate Fraction, to appear in the SIAM Journal on Mathematical Analysis + https://arxiv.org/abs/2010.13882 + http://ian.jauslin.org/publications/20cjl + + * E.A. Carlen, M. Holzmann, I. Jauslin, E.H. Lieb - Simplified approach to + the repulsive Bose gas from low to high densities and its numerical + accuracy, Physical Review A, volume 103, issue 5, number 053309, 2021 + https://doi.org/10.1103/PhysRevA.103.053309 + https://arxiv.org/abs/2011.10869 + http://ian.jauslin.org/publications/20chjl + + + +Dependencies: + + * julia interpreter + Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and other contributors: https://github.com/JuliaLang/julia/contributors + https://julialang.org/ + MIT and GNU GPLv2 Licenses + * julia packages: + FastGaussQuadrature + Alex Townsend + https://github.com/JuliaApproximation/FastGaussQuadrature.jl + MIT license + Polynomials + Jameson Nash and others + https://github.com/JuliaMath/Polynomials.jl + MIT license + SpecialFunctions + Jeff Bezanson, Stefan Karpinski, Viral B. Shah, and others + https://github.com/JuliaMath/SpecialFunctions.jl/ + MIT License + + to typeset the documentation: + * pdftex + pdfTeX team, TeX Live Team, Hàn Thế Thành + http://tug.org/applications/pdftex/ + GNU GPL + * latex + LaTeX3 Project + https://www.latex-project.org/ + LaTeX Project Public License 1.3c + * LaTeX packages: + color + David Carlisle, LaTeX3 Project + https://ctan.org/pkg/color + LaTeX Project Public License 1.3c + marginnote + Markus Kohm + https://komascript.de/marginnote + LaTeX Project Public License 1.3c + amsfonts + American Mathematical Society + http://www.ams.org/tex/amsfonts.html + SIL Open Font Licence + hyperref + Sebastian Rahtz, Heiko Oberdiek, LaTeX3 Project + https://github.com/latex3/hyperref + LaTeX Project Public License 1.3 + array + LaTeX project, Frank Mittelbach + https://ctan.org/pkg/array + LaTeX Project Public License 1.3 + doublestroke + Olaf Kummer + https://ctan.org/pkg/doublestroke + Custom Free license + * optionally: GNU Make + Richard Stallman, Roland McGrath, Paul Smith + https://www.gnu.org/software/make/ + GNU GPLv3 diff --git a/doc/Makefile b/doc/Makefile new file mode 100644 index 0000000..4b58b3c --- /dev/null +++ b/doc/Makefile @@ -0,0 +1,54 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +PROJECTNAME=$(basename $(wildcard *.tex)) +LIBS=$(notdir $(wildcard libs/*)) + +PDFS=$(addsuffix .pdf, $(PROJECTNAME)) +SYNCTEXS=$(addsuffix .synctex.gz, $(PROJECTNAME)) + +all: $(PROJECTNAME) + +$(PROJECTNAME): $(LIBS) + pdflatex -file-line-error $@.tex + pdflatex -file-line-error $@.tex + pdflatex -synctex=1 $@.tex + +$(PROJECTNAME).aux: $(LIBS) + pdflatex -file-line-error -draftmode $(PROJECTNAME).tex + + +$(SYNCTEXS): $(LIBS) + pdflatex -synctex=1 $(patsubst %.synctex.gz, %.tex, $@) + + +libs: $(LIBS) + +$(LIBS): + ln -fs libs/$@ ./ + + +clean-aux: + rm -f $(addsuffix .aux, $(PROJECTNAME)) + rm -f $(addsuffix .log, $(PROJECTNAME)) + rm -f $(addsuffix .out, $(PROJECTNAME)) + rm -f $(addsuffix .toc, $(PROJECTNAME)) + +clean-libs: + rm -f $(LIBS) + +clean-tex: + rm -f $(PDFS) $(SYNCTEXS) + +clean: clean-aux clean-tex clean-libs diff --git a/doc/bibliography/bibliography.tex b/doc/bibliography/bibliography.tex new file mode 100644 index 0000000..53454da --- /dev/null +++ b/doc/bibliography/bibliography.tex @@ -0,0 +1,13 @@ +\bibitem[CHe21]{CHe21}E.A. Carlen, M. Holzmann, I. Jauslin, E.H. Lieb - {\it Simplified approach to the repulsive Bose gas from low to high densities and its numerical accuracy}, Physical Review A, volume~\-103, issue~\-5, number~\-053309, 2021,\par\penalty10000 +doi:{\tt\color{blue}\href{http://dx.doi.org/10.1103/PhysRevA.103.053309}{10.1103/PhysRevA.103.053309}}, arxiv:{\tt\color{blue}\href{http://arxiv.org/abs/2011.10869}{2011.10869}}.\par\medskip + +\bibitem[CJL20]{CJL20}E.A. Carlen, I. Jauslin, E.H. Lieb - {\it Analysis of a simple equation for the ground state energy of the Bose gas}, Pure and Applied Analysis, volume~\-2, issue~\-3, pages~\-659-684, 2020,\par\penalty10000 +doi:{\tt\color{blue}\href{http://dx.doi.org/10.2140/paa.2020.2.659}{10.2140/paa.2020.2.659}}, arxiv:{\tt\color{blue}\href{http://arxiv.org/abs/1912.04987}{1912.04987}}.\par\medskip + +\bibitem[CJL20b]{CJL20b}E.A. Carlen, I. Jauslin, E.H. Lieb - {\it Analysis of a simple equation for the ground state of the Bose gas II: Monotonicity, Convexity and Condensate Fraction}, 2020, to appear in the SIAM journal of Mathematical Analysis,\par\penalty10000 +arxiv:{\tt\color{blue}\href{http://arxiv.org/abs/2010.13882}{2010.13882}}.\par\medskip + +\bibitem[DLMF]{DLMF1.1.3}F.W.J. Olver, A.B. Olde Daalhuis, D.W. Lozier, B.I. Schneider, R.F. Boisvert, C.W. Clark, B.R. Miller, B.V. Saunders, H.S. Cohl, M.A. McClain (editors) - {\it NIST Digital Library of Mathematical Functions}, Release~\-1.1.3 of~\-2021-09-15, 2021.\par\medskip + +\bibitem[Ta87]{Ta87}Y. Taguchi - {\it Fourier coefficients of periodic functions of Gevrey classes and ultradistributions}, Yokohama Mathematical Journal, volume~\-35, pages~\-51-60, 1987.\par\medskip + diff --git a/doc/libs/code.sty b/doc/libs/code.sty new file mode 100644 index 0000000..d4f4070 --- /dev/null +++ b/doc/libs/code.sty @@ -0,0 +1,43 @@ +%% Copyright 2021 Ian Jauslin +%% +%% Licensed under the Apache License, Version 2.0 (the "License"); +%% you may not use this file except in compliance with the License. +%% You may obtain a copy of the License at +%% +%% http://www.apache.org/licenses/LICENSE-2.0 +%% +%% Unless required by applicable law or agreed to in writing, software +%% distributed under the License is distributed on an "AS IS" BASIS, +%% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +%% See the License for the specific language governing permissions and +%% limitations under the License. + +%% +%% Code package: +%% commands to typeset code +%% + +%% TeX format +\NeedsTeXFormat{LaTeX2e}[1995/12/01] + +%% package name +\ProvidesPackage{code}[2021/03/15] + +%% code environment +\def\code{ + \par + \leftskip10pt + \bigskip + \tt +} +\def\endcode{ + \rm + \par + \bigskip + \leftskip0pt +} + + +%% end +\endinput + diff --git a/doc/libs/dlmf.sty b/doc/libs/dlmf.sty new file mode 100644 index 0000000..f435e96 --- /dev/null +++ b/doc/libs/dlmf.sty @@ -0,0 +1,55 @@ +%% Copyright 2021 Ian Jauslin +%% +%% Licensed under the Apache License, Version 2.0 (the "License"); +%% you may not use this file except in compliance with the License. +%% You may obtain a copy of the License at +%% +%% http://www.apache.org/licenses/LICENSE-2.0 +%% +%% Unless required by applicable law or agreed to in writing, software +%% distributed under the License is distributed on an "AS IS" BASIS, +%% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +%% See the License for the specific language governing permissions and +%% limitations under the License. + +%% +%% DLMF package: +%% cite equations from DLMF +%% + +%% TeX format +\NeedsTeXFormat{LaTeX2e}[1995/12/01] + +%% package name +\ProvidesPackage{dlmf}[2020/05/01] + +%% dependencies +\RequirePackage{color} +\RequirePackage{hyperref} + +% get the first two numbers in (a.b.c); +\def\@sectionnr(#1.#2.#3){#1.#2} + +% get the last number in (a.b.c); +\def\@eqnr(#1.#2.#3){#3} + +% remove parentheses around argument +\def\@cleanparentheses(#1){#1} + +%% cite DLMF equation +\def\dlmfcite#1#2{\leavevmode% + \let\@dlmfcite@separator\@empty% + \cite[% + % loop over ',' separated list + \@for\@dlmfcite:=#1\do{% + % put commas between entries + \@dlmfcite@separator\def\@dlmfcite@separator{,\ }% + ({\color{blue}\href{https://dlmf.nist.gov/\expandafter\@sectionnr\@dlmfcite\#E\expandafter\@eqnr\@dlmfcite}{\expandafter\@cleanparentheses\@dlmfcite}})% + }% + ]{DLMF#2}% +} + +%% end +\endinput + + diff --git a/doc/libs/ian.cls b/doc/libs/ian.cls new file mode 100644 index 0000000..f29e6bd --- /dev/null +++ b/doc/libs/ian.cls @@ -0,0 +1,682 @@ +%% +%% Ian's class file +%% + +%% TeX format +\NeedsTeXFormat{LaTeX2e}[1995/12/01] + +%% class name +\ProvidesClass{ian}[2017/09/29] + +%% boolean to signal that this class is being used +\newif\ifianclass + +%% options +% no section numbering in equations +\DeclareOption{section_in_eq}{\sectionsineqtrue} +\DeclareOption{section_in_fig}{\sectionsinfigtrue} +\DeclareOption{section_in_theo}{\PassOptionsToPackage{\CurrentOption}{iantheo}} +\DeclareOption{section_in_all}{\sectionsineqtrue\sectionsinfigtrue\PassOptionsToPackage{section_in_theo}{iantheo}} +\DeclareOption{subsection_in_eq}{\subsectionsineqtrue} +\DeclareOption{subsection_in_fig}{\subsectionsinfigtrue} +\DeclareOption{subsection_in_theo}{\PassOptionsToPackage{\CurrentOption}{iantheo}} +\DeclareOption{subsection_in_all}{\subsectionsineqtrue\subsectionsinfigtrue\PassOptionsToPackage{subsection_in_theo}{iantheo}} +\DeclareOption{no_section_in_eq}{\sectionsineqfalse} +\DeclareOption{no_section_in_fig}{\sectionsinfigfalse} +\DeclareOption{no_section_in_theo}{\PassOptionsToPackage{\CurrentOption}{iantheo}} +\DeclareOption{no_section_in_all}{\sectionsineqfalse\sectionsinfigfalse\PassOptionsToPackage{no_section_in_theo}{iantheo}} +\DeclareOption{no_subsection_in_eq}{\subsectionsineqfalse} +\DeclareOption{no_subsection_in_fig}{\subsectionsinfigfalse} +\DeclareOption{no_subsection_in_theo}{\PassOptionsToPackage{\CurrentOption}{iantheo}} +\DeclareOption{no_subsection_in_all}{\subsectionsineqfalse\subsectionsinfigfalse\PassOptionsToPackage{no_subsection_in_theo}{iantheo}} +% reset point +\DeclareOption{point_reset_at_section}{\PassOptionsToPackage{reset_at_section}{point}} +\DeclareOption{point_no_reset_at_section}{\PassOptionsToPackage{no_reset_at_section}{point}} +\DeclareOption{point_reset_at_theo}{\PassOptionsToPackage{reset_at_theo}{point}} +\DeclareOption{point_no_reset_at_theo}{\PassOptionsToPackage{no_reset_at_theo}{point}} + +\def\ian@defaultoptions{ + \ExecuteOptions{section_in_all, no_subsection_in_all} + \ProcessOptions + + %% required packages + \RequirePackage{iantheo} + \RequirePackage{point} + \RequirePackage{color} + \RequirePackage{marginnote} + \RequirePackage{amssymb} + \PassOptionsToPackage{hidelinks}{hyperref} + \RequirePackage{hyperref} +} + +%% paper dimensions +\setlength\paperheight{297mm} +\setlength\paperwidth{210mm} + +%% fonts +\input{size11.clo} +\DeclareOldFontCommand{\rm}{\normalfont\rmfamily}{\mathrm} +\DeclareOldFontCommand{\sf}{\normalfont\sffamily}{\mathsf} +\DeclareOldFontCommand{\tt}{\normalfont\ttfamily}{\mathtt} +\DeclareOldFontCommand{\bf}{\normalfont\bfseries}{\mathbf} +\DeclareOldFontCommand{\it}{\normalfont\itshape}{\mathit} + +%% text dimensions +\hoffset=-50pt +\voffset=-72pt +\textwidth=460pt +\textheight=704pt + + +%% remove default indentation +\parindent=0pt +%% indent command +\def\indent{\hskip20pt} + +%% something is wrong with \thepage, redefine it +\gdef\thepage{\the\c@page} + +%% array lines (to use the array environment) +\setlength\arraycolsep{5\p@} +\setlength\arrayrulewidth{.4\p@} + + +%% correct vertical alignment at the end of a document +\AtEndDocument{ + \vfill + \eject +} + + +%% hyperlinks +% hyperlinkcounter +\newcounter{lncount} +% hyperref anchor +\def\hrefanchor{% +\stepcounter{lncount}% +\hypertarget{ln.\thelncount}{}% +} + +%% define a command and write it to aux file +\def\outdef#1#2{% + % define command% + \expandafter\xdef\csname #1\endcsname{#2}% + % hyperlink number% + \expandafter\xdef\csname #1@hl\endcsname{\thelncount}% + % write command to aux% + \immediate\write\@auxout{\noexpand\expandafter\noexpand\gdef\noexpand\csname #1\endcsname{\csname #1\endcsname}}% + \immediate\write\@auxout{\noexpand\expandafter\noexpand\gdef\noexpand\csname #1@hl\endcsname{\thelncount}}% +} + +%% can call commands even when they are not defined +\def\safe#1{% + \ifdefined#1% + #1% + \else% + {\color{red}\bf?}% + \fi% +} + +%% define a label for the latest tag +%% label defines a command containing the string stored in \tag +\def\deflabel{ + \def\label##1{\expandafter\outdef{label@##1}{\safe\tag}} + + \def\ref##1{% + % check whether the label is defined (hyperlink runs into errors if this check is omitted) + \ifcsname label@##1@hl\endcsname% + \hyperlink{ln.\csname label@##1@hl\endcsname}{{\color{blue}\safe\csname label@##1\endcsname}}% + \else% + \ifcsname label@##1\endcsname% + {\color{blue}\csname ##1\endcsname}% + \else% + {\bf ??}% + \fi% + \fi% + } + % manually specify label for ref + \def\refname##1##2{% + % check whether the label is defined (hyperlink runs into errors if this check is omitted) + \ifcsname label@##1@hl\endcsname% + \hyperlink{ln.\csname label@##1@hl\endcsname}{{\color{blue}##2}}% + \else% + ??##2% + \fi% + } +} + + +%% make a custom link at any given location in the document +\def\makelink#1#2{% + \hrefanchor% + \outdef{label@#1}{#2}% +} + + +%% section command +% counter +\newcounter{sectioncount} +% space before section +\newlength\secskip +\setlength\secskip{40pt} +% a prefix to put before the section number, e.g. A for appendices +\def\sectionprefix{} +% define some lengths +\newlength\secnumwidth +\newlength\sectitlewidth +\def\section#1{ + % reset counters + \stepcounter{sectioncount} + \setcounter{subsectioncount}{0} + \ifsectionsineq + \setcounter{seqcount}0 + \fi + \ifsectionsinfig + \setcounter{figcount}0 + \fi + + % space before section (if not first) + \ifnum\thesectioncount>1 + \vskip\secskip + \penalty-1000 + \fi + + % hyperref anchor + \hrefanchor + % define tag (for \label) + \xdef\tag{\sectionprefix\thesectioncount} + + % get widths + \def\@secnum{{\bf\Large\sectionprefix\thesectioncount.\hskip10pt}} + \settowidth\secnumwidth{\@secnum} + \setlength\sectitlewidth\textwidth + \addtolength\sectitlewidth{-\secnumwidth} + + % print name + \parbox{\textwidth}{ + \@secnum + \parbox[t]{\sectitlewidth}{\Large\bf #1}} + + % write to table of contents + \iftoc + % save lncount in aux variable which is written to toc + \immediate\write\tocoutput{\noexpand\expandafter\noexpand\edef\noexpand\csname toc@sec.\thesectioncount\endcsname{\thelncount}} + \write\tocoutput{\noexpand\tocsection{#1}{\thepage}} + \fi + + %space + \par\penalty10000 + \bigskip\penalty10000 +} + +%% subsection +% counter +\newcounter{subsectioncount} +% space before subsection +\newlength\subsecskip +\setlength\subsecskip{30pt} +\def\subsection#1{ + % counters + \stepcounter{subsectioncount} + \setcounter{subsubsectioncount}{0} + \ifsubsectionsineq + \setcounter{seqcount}0 + \fi + \ifsubsectionsinfig + \setcounter{figcount}0 + \fi + + % space before subsection (if not first) + \ifnum\thesubsectioncount>1 + \vskip\subsecskip + \penalty-500 + \fi + + % hyperref anchor + \hrefanchor + % define tag (for \label) + \xdef\tag{\sectionprefix\thesectioncount.\thesubsectioncount} + + % get widths + \def\@secnum{{\bf\large\hskip.5cm\sectionprefix\thesectioncount.\thesubsectioncount.\hskip5pt}} + \settowidth\secnumwidth{\@secnum} + \setlength\sectitlewidth\textwidth + \addtolength\sectitlewidth{-\secnumwidth} + % print name + \parbox{\textwidth}{ + \@secnum + \parbox[t]{\sectitlewidth}{\large\bf #1}} + + % write to table of contents + \iftoc + % save lncount in aux variable which is written to toc + \immediate\write\tocoutput{\noexpand\expandafter\noexpand\edef\noexpand\csname toc@subsec.\thesectioncount.\thesubsectioncount\endcsname{\thelncount}} + \write\tocoutput{\noexpand\tocsubsection{#1}{\thepage}} + \fi + + % space + \par\penalty10000 + \medskip\penalty10000 +} + +%% subsubsection +% counter +\newcounter{subsubsectioncount} +% space before subsubsection +\newlength\subsubsecskip +\setlength\subsubsecskip{20pt} +\def\subsubsection#1{ + % counters + \stepcounter{subsubsectioncount} + + % space before subsubsection (if not first) + \ifnum\thesubsubsectioncount>1 + \vskip\subsubsecskip + \penalty-500 + \fi + + % hyperref anchor + \hrefanchor + % define tag (for \label) + \xdef\tag{\sectionprefix\thesectioncount.\thesubsectioncount.\thesubsubsectioncount} + + % get widths + \def\@secnum{{\bf\hskip1.cm\sectionprefix\thesectioncount.\thesubsectioncount.\thesubsubsectioncount.\hskip5pt}} + \settowidth\secnumwidth{\@secnum} + \setlength\sectitlewidth\textwidth + \addtolength\sectitlewidth{-\secnumwidth} + % print name + \parbox{\textwidth}{ + \@secnum + \parbox[t]{\sectitlewidth}{\large\bf #1}} + + % write to table of contents + \iftoc + % save lncount in aux variable which is written to toc + \immediate\write\tocoutput{\noexpand\expandafter\noexpand\edef\noexpand\csname toc@subsubsec.\thesectioncount.\thesubsectioncount.\thesubsubsectioncount\endcsname{\thelncount}} + \write\tocoutput{\noexpand\tocsubsubsection{#1}{\thepage}} + \fi + + % space + \par\penalty10000 + \medskip\penalty10000 +} + +%% itemize +\newlength\itemizeskip +% left margin for items +\setlength\itemizeskip{20pt} +\newlength\itemizeseparator +% space between the item symbol and the text +\setlength\itemizeseparator{5pt} +% penalty preceding an itemize +\newcount\itemizepenalty +\itemizepenalty=0 +% counter counting the itemize level +\newcounter{itemizecount} + +% item symbol +\def\itemizept#1{ + \ifnum#1=1 + \textbullet + \else + $\scriptstyle\blacktriangleright$ + \fi +} + + +\newlength\current@itemizeskip +\setlength\current@itemizeskip{0pt} +\def\itemize{% + \par\expandafter\penalty\the\itemizepenalty\medskip\expandafter\penalty\the\itemizepenalty% + \addtocounter{itemizecount}{1}% + \addtolength\current@itemizeskip{\itemizeskip}% + \leftskip\current@itemizeskip% +} +\def\enditemize{% + \addtocounter{itemizecount}{-1}% + \addtolength\current@itemizeskip{-\itemizeskip}% + \par\expandafter\penalty\the\itemizepenalty\leftskip\current@itemizeskip% + \medskip\expandafter\penalty\the\itemizepenalty% +} + +% item, with optional argument to specify the item point +% @itemarg is set to true when there is an optional argument +\newif\if@itemarg +\def\item{% + % check whether there is an optional argument (if there is none, add on empty '[]') + \@ifnextchar [{\@itemargtrue\@itemx}{\@itemargfalse\@itemx[]}% +} +\newlength\itempt@total +\def\@itemx[#1]{ + \if@itemarg + \settowidth\itempt@total{#1} + \else + \settowidth\itempt@total{\itemizept\theitemizecount} + \fi + \addtolength\itempt@total{\itemizeseparator} + \par + \medskip + \if@itemarg + \hskip-\itempt@total#1\hskip\itemizeseparator + \else + \hskip-\itempt@total\itemizept\theitemizecount\hskip\itemizeseparator + \fi +} + +%% prevent page breaks after itemize +\newcount\previtemizepenalty +\def\nopagebreakafteritemize{ + \previtemizepenalty=\itemizepenalty + \itemizepenalty=10000 +} +%% back to previous value +\def\restorepagebreakafteritemize{ + \itemizepenalty=\previtemizepenalty +} + +%% enumerate +\newcounter{enumerate@count} +\def\enumerate{ + \setcounter{enumerate@count}0 + \let\olditem\item + \let\olditemizept\itemizept + \def\item{ + % counter + \stepcounter{enumerate@count} + % set header + \def\itemizept{\theenumerate@count.} + % hyperref anchor + \hrefanchor + % define tag (for \label) + \xdef\tag{\theenumerate@count} + \olditem + } + \itemize +} +\def\endenumerate{ + \enditemize + \let\item\olditem + \let\itemizept\olditemizept +} + + +%% equation numbering +% counter +\newcounter{seqcount} +% booleans (write section or subsection in equation number) +\newif\ifsectionsineq +\newif\ifsubsectionsineq +\def\seqcount{ + \stepcounter{seqcount} + % the output + \edef\seqformat{\theseqcount} + % add subsection number + \ifsubsectionsineq + \let\tmp\seqformat + \edef\seqformat{\thesubsectioncount.\tmp} + \fi + % add section number + \ifsectionsineq + \let\tmp\seqformat + \edef\seqformat{\sectionprefix\thesectioncount.\tmp} + \fi + % define tag (for \label) + \xdef\tag{\seqformat} + % write number + \marginnote{\hfill(\seqformat)} +} +%% equation environment compatibility +\def\equation{\hrefanchor$$\seqcount} +\def\endequation{$$\@ignoretrue} + + +%% figures +% counter +\newcounter{figcount} +% booleans (write section or subsection in equation number) +\newif\ifsectionsinfig +\newif\ifsubsectionsinfig +% width of figures +\newlength\figwidth +\setlength\figwidth\textwidth +\addtolength\figwidth{-2.5cm} +% caption +\def\defcaption{ + \long\def\caption##1{ + \stepcounter{figcount} + + % hyperref anchor + \hrefanchor + + % the number of the figure + \edef\figformat{\thefigcount} + % add subsection number + \ifsubsectionsinfig + \let\tmp\figformat + \edef\figformat{\thesubsectioncount.\tmp} + \fi + % add section number + \ifsectionsinfig + \let\tmp\figformat + \edef\figformat{\sectionprefix\thesectioncount.\tmp} + \fi + + % define tag (for \label) + \xdef\tag{\figformat} + + % write + \hfil fig \figformat: \parbox[t]{\figwidth}{\leavevmode\small##1} + + % space + \par\bigskip + } +} +%% short caption: centered +\def\captionshort#1{ + \stepcounter{figcount} + + % hyperref anchor + \hrefanchor + + % the number of the figure + \edef\figformat{\thefigcount} + % add section number + \ifsectionsinfig + \let\tmp\figformat + \edef\figformat{\sectionprefix\thesectioncount.\tmp} + \fi + + % define tag (for \label) + \xdef\tag{\figformat} + + % write + \hfil fig \figformat: {\small#1} + + %space + \par\bigskip +} + +%% environment +\def\figure{ + \par + \vfil\penalty100\vfilneg + \bigskip +} +\def\endfigure{ + \par + \bigskip +} + + +%% start appendices +\def\appendix{ + \vfill + \pagebreak + + % counter + \setcounter{sectioncount}0 + + % prefix + \def\sectionprefix{A} + + % write + {\bf \LARGE Appendices}\par\penalty10000\bigskip\penalty10000 + + % add a mention in the table of contents + \iftoc + \immediate\write\tocoutput{\noexpand\tocappendices}\penalty10000 + \fi + + %% uncomment for new page for each appendix + %\def\seqskip{\vfill\pagebreak} +} + + +%% bibliography +% size of header +\newlength\bibheader +\def\thebibliography#1{ + \hrefanchor + + % add a mention in the table of contents + \iftoc + % save lncount in aux variable which is written to toc + \immediate\write\tocoutput{\noexpand\expandafter\noexpand\edef\noexpand\csname toc@references\endcsname{\thelncount}} + \write\tocoutput{\noexpand\tocreferences{\thepage}}\penalty10000 + \fi + + % write + {\bf \LARGE References}\par\penalty10000\bigskip\penalty10000 + % width of header + \settowidth\bibheader{[#1]} + \leftskip\bibheader +} +% end environment +\def\endthebibliography{ + \par\leftskip0pt +} + +%% bibitem command +\def\bibitem[#1]#2{% + \hrefanchor% + \outdef{label@cite#2}{#1}% + \hskip-\bibheader% + \makebox[\bibheader]{\cite{#2}\hfill}% +} + +%% cite command +% @tempswa is set to true when there is an optional argument +\newif\@tempswa +\def\cite{% + % check whether there is an optional argument (if there is none, add on empty '[]') + \@ifnextchar [{\@tempswatrue\@citex}{\@tempswafalse\@citex[]}% +} +% command with optional argument +\def\@citex[#1]#2{\leavevmode% + % initialize loop + \let\@cite@separator\@empty% + % format + \@cite{% + % loop over ',' separated list + \@for\@cite@:=#2\do{% + % text to add at each iteration of the loop (separator between citations) + \@cite@separator\def\@cite@separator{,\ }% + % add entry to citelist + \@writecitation{\@cite@}% + \ref{cite\@cite@}% + }% + }% + % add optional argument text (as an argument to '\@cite') + {#1}% +} +\def\@cite#1#2{% + [#1\if@tempswa , #2\fi]% +} +%% add entry to citelist after checking it has not already been added +\def\@writecitation#1{% + \ifcsname if#1cited\endcsname% + \else% + \expandafter\newif\csname if#1cited\endcsname% + \immediate\write\@auxout{\string\citation{#1}}% + \fi% +} + +%% table of contents +% boolean +\newif\iftoc +\def\tableofcontents{ + {\bf \large Table of contents:}\par\penalty10000\bigskip\penalty10000 + + % copy content from file + \IfFileExists{\jobname.toc}{\input{\jobname.toc}}{{\tt error: table of contents missing}} + + % open new toc + \newwrite\tocoutput + \immediate\openout\tocoutput=\jobname.toc + + \toctrue +} +%% close file +\AtEndDocument{ + % close toc + \iftoc + \immediate\closeout\tocoutput + \fi +} + + +%% fill line with dots +\def\leaderfill{\leaders\hbox to 1em {\hss. \hss}\hfill} + +%% same as sectionprefix +\def\tocsectionprefix{} + +%% toc formats +\newcounter{tocsectioncount} +\def\tocsection #1#2{ + \stepcounter{tocsectioncount} + \setcounter{tocsubsectioncount}{0} + \setcounter{tocsubsubsectioncount}{0} + % write + \smallskip\hyperlink{ln.\csname toc@sec.\thetocsectioncount\endcsname}{{\bf \tocsectionprefix\thetocsectioncount}.\hskip5pt {\color{blue}#1}\leaderfill#2}\par +} +\newcounter{tocsubsectioncount} +\def\tocsubsection #1#2{ + \stepcounter{tocsubsectioncount} + \setcounter{tocsubsubsectioncount}{0} + % write + {\hskip10pt\hyperlink{ln.\csname toc@subsec.\thetocsectioncount.\thetocsubsectioncount\endcsname}{{\bf \thetocsectioncount.\thetocsubsectioncount}.\hskip5pt {\color{blue}\small #1}\leaderfill#2}}\par +} +\newcounter{tocsubsubsectioncount} +\def\tocsubsubsection #1#2{ + \stepcounter{tocsubsubsectioncount} + % write + {\hskip20pt\hyperlink{ln.\csname toc@subsubsec.\thetocsectioncount.\thetocsubsectioncount.\thetocsubsubsectioncount\endcsname}{{\bf \thetocsectioncount.\thetocsubsectioncount.\thetocsubsubsectioncount}.\hskip5pt {\color{blue}\small #1}\leaderfill#2}}\par +} +\def\tocappendices{ + \medskip + \setcounter{tocsectioncount}0 + {\bf Appendices}\par + \smallskip + \def\tocsectionprefix{A} +} +\def\tocreferences#1{ + \medskip + {\hyperlink{ln.\csname toc@references\endcsname}{{\color{blue}\bf References}\leaderfill#1}}\par + \smallskip +} + + +%% definitions that must be loaded at begin document +\let\ian@olddocument\document +\def\document{ + \ian@olddocument + + \deflabel + \defcaption +} + +%% end +\ian@defaultoptions +\endinput diff --git a/doc/libs/iantheo.sty b/doc/libs/iantheo.sty new file mode 100644 index 0000000..1945a5f --- /dev/null +++ b/doc/libs/iantheo.sty @@ -0,0 +1,176 @@ +%% Copyright 2021 Ian Jauslin +%% +%% Licensed under the Apache License, Version 2.0 (the "License"); +%% you may not use this file except in compliance with the License. +%% You may obtain a copy of the License at +%% +%% http://www.apache.org/licenses/LICENSE-2.0 +%% +%% Unless required by applicable law or agreed to in writing, software +%% distributed under the License is distributed on an "AS IS" BASIS, +%% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +%% See the License for the specific language governing permissions and +%% limitations under the License. + +%% +%% iantheorem package: +%% Ian's customized theorem command +%% + +%% boolean to signal that this package was loaded +\newif\ifiantheo + +%% TeX format +\NeedsTeXFormat{LaTeX2e}[1995/12/01] + +%% package name +\ProvidesPackage{iantheo}[2016/11/10] + +%% options +\newif\ifsectionintheo +\DeclareOption{section_in_theo}{\sectionintheotrue} +\DeclareOption{no_section_in_theo}{\sectionintheofalse} +\newif\ifsubsectionintheo +\DeclareOption{subsection_in_theo}{\subsectionintheotrue} +\DeclareOption{no_subsection_in_theo}{\subsectionintheofalse} + +\def\iantheo@defaultoptions{ + \ExecuteOptions{section_in_theo, no_subsection_in_theo} + \ProcessOptions + + %%% reset at every new section + \ifsectionintheo + \let\iantheo@oldsection\section + \gdef\section{\setcounter{theocount}{0}\iantheo@oldsection} + \fi + + %% reset at every new subsection + \ifsubsectionintheo + \let\iantheo@oldsubsection\subsection + \gdef\subsection{\setcounter{theocount}{0}\iantheo@oldsubsection} + \fi +} + + +%% delimiters +\def\delimtitle#1{ + \par% + \leavevmode% + \raise.3em\hbox to\hsize{% + \lower0.3em\hbox{\vrule height0.3em}% + \hrulefill% + \ \lower.3em\hbox{#1}\ % + \hrulefill% + \lower0.3em\hbox{\vrule height0.3em}% + }% + \par\penalty10000% +} + +%% callable by ref +\def\delimtitleref#1{ + \par% +% + \ifdefined\ianclass% + % hyperref anchor% + \hrefanchor% + % define tag (for \label)% + \xdef\tag{#1}% + \fi% +% + \leavevmode% + \raise.3em\hbox to\hsize{% + \lower0.3em\hbox{\vrule height0.3em}% + \hrulefill% + \ \lower.3em\hbox{\bf #1}\ % + \hrulefill% + \lower0.3em\hbox{\vrule height0.3em}% + }% + \par\penalty10000% +} + +%% no title +\def\delim{ + \par% + \leavevmode\raise.3em\hbox to\hsize{% + \lower0.3em\hbox{\vrule height0.3em}% + \hrulefill% + \lower0.3em\hbox{\vrule height0.3em}% + }% + \par\penalty10000% +} + +%% end delim +\def\enddelim{ + \par\penalty10000% + \leavevmode% + \raise.3em\hbox to\hsize{% + \vrule height0.3em\hrulefill\vrule height0.3em% + }% + \par% +} + + +%% theorem +% counter +\newcounter{theocount} +% booleans (write section or subsection in equation number) +\def\theo#1{ + \stepcounter{theocount} + \ifdefined\ianclass + % hyperref anchor + \hrefanchor + \fi + % the number + \def\formattheo{\thetheocount} + % add subsection number + \ifsubsectionintheo + \let\tmp\formattheo + \edef\formattheo{\thesubsectioncount.\tmp} + \fi + % add section number + \ifsectionintheo + \let\tmp\formattheo + \edef\formattheo{\sectionprefix\thesectioncount.\tmp} + \fi + % define tag (for \label) + \xdef\tag{\formattheo} + % write + \delimtitle{\bf #1 \formattheo} +} +\let\endtheo\enddelim +%% theorem headers with name +\def\theoname#1#2{ + \theo{#1}\hfil({\it #2})\par\penalty10000\medskip% +} + + +%% qed symbol +\def\qedsymbol{$\square$} +\def\qed{\penalty10000\hfill\penalty10000\qedsymbol} + + +%% compatibility with article class +\ifdefined\ianclasstrue + \relax +\else + \def\thesectioncount{\thesection} + \def\thesubsectioncount{\thesubsection} + \def\sectionprefix{} +\fi + + +%% prevent page breaks after displayed equations +\newcount\prevpostdisplaypenalty +\def\nopagebreakaftereq{ + \prevpostdisplaypenalty=\postdisplaypenalty + \postdisplaypenalty=10000 +} +%% back to previous value +\def\restorepagebreakaftereq{ + \postdisplaypenalty=\prevpostdisplaypenalty +} + + +%% end +\iantheo@defaultoptions +\endinput diff --git a/doc/libs/largearray.sty b/doc/libs/largearray.sty new file mode 100644 index 0000000..cf9075f --- /dev/null +++ b/doc/libs/largearray.sty @@ -0,0 +1,33 @@ +%% Copyright 2021 Ian Jauslin +%% +%% Licensed under the Apache License, Version 2.0 (the "License"); +%% you may not use this file except in compliance with the License. +%% You may obtain a copy of the License at +%% +%% http://www.apache.org/licenses/LICENSE-2.0 +%% +%% Unless required by applicable law or agreed to in writing, software +%% distributed under the License is distributed on an "AS IS" BASIS, +%% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +%% See the License for the specific language governing permissions and +%% limitations under the License. + +%% +%% largearray package: +%% Array spanning the entire line +%% + +%% TeX format +\NeedsTeXFormat{LaTeX2e}[1995/12/01] + +%% package name +\ProvidesPackage{largearray}[2016/11/10] + +\RequirePackage{array} + +%% array spanning the entire line +\newlength\largearray@width +\setlength\largearray@width\textwidth +\addtolength\largearray@width{-10pt} +\def\largearray{\begin{array}{@{}>{\displaystyle}l@{}}\hphantom{\hspace{\largearray@width}}\\[-.5cm]} +\def\endlargearray{\end{array}} diff --git a/doc/libs/point.sty b/doc/libs/point.sty new file mode 100644 index 0000000..a396d1c --- /dev/null +++ b/doc/libs/point.sty @@ -0,0 +1,128 @@ +%% Copyright 2021 Ian Jauslin +%% +%% Licensed under the Apache License, Version 2.0 (the "License"); +%% you may not use this file except in compliance with the License. +%% You may obtain a copy of the License at +%% +%% http://www.apache.org/licenses/LICENSE-2.0 +%% +%% Unless required by applicable law or agreed to in writing, software +%% distributed under the License is distributed on an "AS IS" BASIS, +%% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +%% See the License for the specific language governing permissions and +%% limitations under the License. + +%% +%% Points package: +%% \point commands +%% + +%% TeX format +\NeedsTeXFormat{LaTeX2e}[1995/12/01] + +%% package name +\ProvidesPackage{point}[2017/06/13] + +%% options +\newif\ifresetatsection +\DeclareOption{reset_at_section}{\resetatsectiontrue} +\DeclareOption{no_reset_at_section}{\resetatsectionfalse} +\newif\ifresetatsubsection +\DeclareOption{reset_at_subsection}{\resetatsubsectiontrue} +\DeclareOption{no_reset_at_subsection}{\resetatsubsectionfalse} +\newif\ifresetatsubsubsection +\DeclareOption{reset_at_subsubsection}{\resetatsubsubsectiontrue} +\DeclareOption{no_reset_at_subsubsection}{\resetatsubsubsectionfalse} +\newif\ifresetattheo +\DeclareOption{reset_at_theo}{\resetattheotrue} +\DeclareOption{no_reset_at_theo}{\resetattheofalse} + +\def\point@defaultoptions{ + \ExecuteOptions{reset_at_section, reset_at_subsection, reset_at_subsubsection, no_reset_at_theo} + \ProcessOptions + + %% reset at every new section + \ifresetatsection + \let\point@oldsection\section + \gdef\section{\resetpointcounter\point@oldsection} + \fi + %% reset at every new subsection + \ifresetatsubsection + \let\point@oldsubsection\subsection + \gdef\subsection{\resetpointcounter\point@oldsubsection} + \fi + %% reset at every new subsubsection + \ifresetatsubsubsection + \let\point@oldsubsubsection\subsubsection + \gdef\subsubsection{\resetpointcounter\point@oldsubsubsection} + \fi + + %% reset at every new theorem + \ifresetattheo + \ifdefined\iantheotrue + \let\point@oldtheo\theo + \gdef\theo{\resetpointcounter\point@oldtheo} + \fi + \fi +} + + +%% point +% counter +\newcounter{pointcount} +\def\point{ + \stepcounter{pointcount} + \setcounter{subpointcount}{0} + % hyperref anchor (only if the class is 'ian') + \ifdefined\ifianclass + \hrefanchor + % define tag (for \label) + \xdef\tag{\thepointcount} + \fi + % header + \indent{\bf \thepointcount\ - } +} + +%% subpoint +% counter +\newcounter{subpointcount} +\def\subpoint{ + \stepcounter{subpointcount} + \setcounter{subsubpointcount}0 + % hyperref anchor (only if the class is 'ian') + \ifdefined\ifianclass + \hrefanchor + % define tag (for \label) + \xdef\tag{\thepointcount-\thesubpointcount} + \fi + % header + \indent\hskip.5cm{\bf \thepointcount-\thesubpointcount\ - } +} + +%% subsubpoint +% counter +\newcounter{subsubpointcount} +\def\subsubpoint{ + \stepcounter{subsubpointcount} + % hyperref anchor (only if the class is 'ian') + \ifdefined\ifianclass + \hrefanchor + % define tag (for \label) + \xdef\tag{\thepointcount-\thesubpointcount-\thesubsubpointcount} + \fi + \indent\hskip1cm{\bf \thepointcount-\thesubpointcount-\thesubsubpointcount\ - } +} + + +%% reset point counters +\def\resetpointcounter{ + \setcounter{pointcount}{0} + \setcounter{subpointcount}{0} + \setcounter{subsubpointcount}{0} +} + + + +%% end +\point@defaultoptions +\endinput diff --git a/doc/simplesolv-doc.tex b/doc/simplesolv-doc.tex new file mode 100644 index 0000000..722282a --- /dev/null +++ b/doc/simplesolv-doc.tex @@ -0,0 +1,3679 @@ +%% Copyright 2021 Ian Jauslin +%% +%% Licensed under the Apache License, Version 2.0 (the "License"); +%% you may not use this file except in compliance with the License. +%% You may obtain a copy of the License at +%% +%% http://www.apache.org/licenses/LICENSE-2.0 +%% +%% Unless required by applicable law or agreed to in writing, software +%% distributed under the License is distributed on an "AS IS" BASIS, +%% WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +%% See the License for the specific language governing permissions and +%% limitations under the License. + +\documentclass[subsection_in_all]{ian} + +\usepackage{code} +\usepackage{dsfont} +\usepackage{largearray} +\usepackage{dlmf} + +\def\Eta{H} + +\begin{document} + +\hbox{} +\hfil{\bf\Large +{\tt simplesolv}\par +\medskip +\hfil \large v0.3 +} + +\vfill + +\tableofcontents + +\vfill +\eject + +\setcounter{page}1 +\pagestyle{plain} + +\indent +{\tt simplesolv} is a tool to compute the solution of the equations of the ``Simplified approach'' to the repulsive Bose gas introduced in\-~\cite{CJL20,CJL20b,CHe21}. +This approach provides an approximation to various observables of the ground state of the Hamiltonian +\begin{equation} + H_N=-\frac12\sum_{i=1}^N\Delta_i+\sum_{1\leqslant i\leqslant j\leqslant N}v(|x_i-x_j|) +\end{equation} +in three dimensions, with periodic boundary conditions, in the thermodynamic limit $N\to\infty$ at fixed density $\rho$. +\bigskip + +\indent +{\tt simplesolv} is written in {\tt julia}. +The source code is located in the {\tt src} directory of this bundle. +Throughout the documentation, we will refer to the directory containing the {\tt src} directory as the ``installation directory'', and will denote it by the bash variable {\tt\$SIMPLESOLV} (so that the main julia file, for instance, is located at {\tt \$SIMPLESOLV/src/main.jl}). +\vskip20pt + +\section{Basic usage} +\indent +Denoting the location of the installation directory by {\tt\$SIMPLESOLV}, {\tt simplesolv} is run by calling +\begin{code} + julia \$SIMPLESOLV/src/main.jl [args] +\end{code} +where the optional arguments {\tt [args]} take the form {\tt [-p params] [-U potential] [-M method] [-s savefile]}. +\bigskip + +\indent +A few commands support multithreaded execution. +To enable {\tt julia} to run on several processors, it should be run with the {\tt -p} option. +For example, to run on 8 CPUs, run +\begin{code} + julia -p 8 \$SIMPLESOLV/src/main.jl [args] +\end{code} +\bigskip + +\indent +{\tt command} specifies which computation is to be carried out, such as {\tt energy} to compute the ground state energy, or {\tt condensate\_fraction} for the uncondensed fraction. +The list of available commands depends on the \refname{sec:methods}{{\tt method}} argument, which specifies one of the available methods to solve the equation at hand. +The available methods are (see section\-~\ref{sec:methods} for further details) +\begin{itemize} + \item \refname{sec:easyeq}{{\tt easyeq}} ({\bf default}) for the Simple or Medium equation, or any interpolation between them, with a soft potential using the Newton algorithm, + \item \refname{sec:anyeq}{{\tt anyeq}} for any equation in the ``Simplified approach'' using the Newton algorithm. + \item \refname{sec:simpleq-Kv}{{\tt simpleq-Kv}} for the Simple equation using explicit expressions involving $\mathfrak Kv$ (see\-~(\ref{Kv})), + \item \refname{sec:simpleq-hardcore}{{\tt simpleq-hardcore}} for the Simple equation with a hard core potential using the Newton algorithm, + \item \refname{sec:simpleq-iteration}{{\tt simpleq-iteration}} for the Simple equation with a soft potential using the iteration defined in\-~\cite{CJL20}. +\end{itemize} +Each method is described in detail below, along with the list of commands ({\tt command}) and parameters ({\tt params}) compatible with them. +\bigskip + +\indent +{\tt params} should be a `{\tt;}' separated list of entries, each of which is of the form {\tt key=value}. +For example {\tt -p "rho=1e-6;v\_a=2"}. +(Note that you should not end the list of parameters by a `{\tt;}', otherwise {\tt simplesolv} will interpret that as there being an empty parameter entry, which it cannot split into a key and value, and will fail.) +\bigskip + +\indent +\refname{sec:potentials}{{\tt potential}} specifies which potential $v$ should be used, from the following list (see section\-~\ref{sec:potentials} for further details). +\begin{itemize} + \item \refname{sec:exp}{{\tt exp}} ({\bf default}) for $v(|x|)=ae^{-|x|}$, + \item \refname{sec:tent}{{\tt tent}} for $v(|x|)=\mathds 1_{|x|0$. +\bigskip + +\indent +\makelink{eq:easyeq_simpleq}{} +\makelink{eq:easyeq_medeq}{} +The special choice $\beta_K=\beta_L=0$ is called the Simple equation ({\tt simpleq}), and the choice $\beta_K=\beta_L=1$ is called the Medium equation ({\tt medeq}) +\bigskip + +\subsubsection{Usage} +\indent +Unless otherwise noted, this method takes the following parameters (specified via the {\tt [-p params]} flag, as a `{\tt;}' separated list of entries). +\begin{itemize} + \item\makelink{param:easyeq_rho}{} + {\tt rho} ({\tt Float64}, default: $10^{-6}$): density $\rho$. + + \item\makelink{param:easyeq_tolerance}{} + {\tt tolerance} ({\tt Float64}, default: $10^{-11}$): maximal size of final step in Newton iteration. + + \item\makelink{param:easyeq_maxiter}{} + {\tt maxiter} ({\tt Int64}, default: 21): maximal number of iterations of the Newton algorithm before giving up. + + \item\makelink{param:easyeq_order}{} + {\tt order} ({\tt Int64}, default: 100): order used for all Gauss quadratures (denoted by $N$ below). + + \item\makelink{param:easyeq_minlrho_init}{} + {\tt minlrho\_init} ({\tt Float64}, default: {\tt rho}): to initialize the Newton algorithm, we first compute the solution for a smaller $\rho$, {\tt minlrho} is the minimal value for $\log_{10}\rho$ to start this initialization process. + + \item\makelink{param:easyeq_nlrho_init}{} + {\tt nlrho\_init} ({\tt Int64}, default: 1): number of steps in the initialization process described above. Set to 1 to disable the incremental initialization process. + + \item\makelink{param:easyeq_bK}{} + {\tt bK}, {\tt bL} ({\tt Float64}, default: 1, 1): the values of $\beta_K$ and $\beta_L$. + + \item\makelink{param:easyeq_eq}{} + {\tt eq} ({\tt String}, default: ``{\tt simpleq}'', acceptable values: ``{\tt simpleq}'', ``{\tt medeq}''): A shortcut to select either the \refname{eq:easyeq_simpleq}{Simple equation} ($\beta_K=\beta_L=0$) or the \refname{eq:easyeq_medeq}{Medium equation} ($\beta_L=\beta_K=1$). When this option is set, neither {\tt bK} nor {\tt bL} should be set. +\end{itemize} +\bigskip + +The available {\tt commands} are the following. + +\begin{itemize} + \item {\tt energy}\makelink{command:easyeq_energy}{}: + compute the energy $e$ at a given $\rho$.\par + \underline{Output}: [$e$] [Newton error $\epsilon$]. + + \item {\tt energy\_rho}\makelink{command:easyeq_energy_rho}{}: + compute the energy $e$ as a function of $\rho$. + The Newton algorithm is initialized with the hardcore scattering solution (\ref{easyeq_init}) for the lowest $\rho$, and with the previously computed $\rho$ for the larger densities.\par + \underline{Disabled parameters}: \refname{param:easyeq_rho}{{\tt rho}}, \refname{param:easyeq_minlrho_init}{{\tt minlrho\_init}} and \refname{param:easyeq_nlrho_init}{{\tt nlrho\_init}}.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:easyeq_minlrho}{} + {\tt minlrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\log_{10}\rho$. + + \item\makelink{param:easyeq_maxlrho}{} + {\tt maxlrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\log_{10}\rho$. + + \item\makelink{param:easyeq_nlrho}{} + {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically). + + \item\makelink{param:easyeq_rhos}{} + {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list. + \end{itemize} + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$]. + + \item\makelink{command:easyeq_condensate_fraction}{} + {\tt condensate\_fraction}: compute the uncondensed fraction $\eta$ at a given $\rho$.\par + \underline{Output}: [$\eta$] [Newton error $\epsilon$]. + + \item\makelink{command:easyeq_condensate_fraction_rho}{} + {\tt condensate\_fraction\_rho}: compute the uncondensed fraction $\eta$ as a function of $\rho$. + The Newton algorithm is initialized with the hardcore scattering solution (\ref{easyeq_init}) for the lowest $\rho$, and with the previously computed $\rho$ for the larger densities.\par + \underline{Disabled parameters}: same as \refname{command:easyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Extra parameters}: same as \refname{command:easyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$\eta$] [Newton error $\epsilon$]. + + \item\makelink{command:easyeq_uk}{} + {\tt uk}: compute the Fourier transform $\hat u(|k|)$. + The values $|k|$ at which $\hat u$ is computed are those coming from the Gauss quadratures, and cannot be set.\par + \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\hat u(|k|)$] + + \item\makelink{command:easyeq_ux}{} + {\tt ux}: compute $u$ as a function of $|x|$.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:easyeq_xmin}{} + {\tt xmin} ({\tt Float64}, default: 0): minimum of the range of $|x|$ to be printed. + + \item\makelink{param:easyeq_xmax}{} + {\tt xmax} ({\tt Float64}, default: 100): maximum of the range of $|x|$ to be printed. + + \item\makelink{param:easyeq_nx}{} + {\tt nx} ({\tt Int64}, default: 100): number of points to print (linearly spaced). + \end{itemize} + \underline{Output} (one line for each value of $x$): [$|x|$] [$u(|x|)$] + + \item\makelink{command:easyeq_uux}{} + {\tt uux}: compute $2u-\rho u\ast u$ as a function of $|x|$.\par + \underline{Extra parameters}: Same as \refname{command:easyeq_ux}{{\tt ux}}.\par + \underline{Output} (one line for each value of $x$): [$|x|$] [$2u(|x|)-\rho u\ast u(|x|)$] + +\end{itemize} + +\subsubsection{Description} +\point{\bf Fourier space formulation.} +The computation is carried out in Fourier space. +We take the convention that the Fourier transform of a function $f(|x|)$ is +\begin{equation} + \hat f(|k|)=\int_{\mathbb R^3} dx\ e^{ikx}f(|x|) + =\frac{4\pi}{|k|}\int_0^\infty dr\ \frac{\sin(|k|r)}rf(r) + . +\end{equation} +In Fourier space, (\ref{easyeq}) becomes +\begin{equation} + k^2\hat u=\hat S-2\rho\hat A_K\hat u+\rho^2\hat A_L\hat u^2 +\end{equation} +\begin{equation} + \hat A_K:=\beta_K\hat S+(1-\beta_K)\frac{2e}\rho + ,\quad + \hat A_L:=\beta_L\hat S+(1-\beta_L)\frac{2e}\rho +\end{equation} +with +\begin{equation} + \hat S(|k|)=\int_{\mathbb R^3} dx\ e^{ikx}(1-u(|x|))v(|x|) + =\hat v(k)-\hat u\hat\ast\hat v(k) + ,\quad + \hat f\hat\ast\hat g(|k|):=\int_{\mathbb R^3}\frac{dp}{(2\pi)^3}\ \hat f(|k-p|)\hat g(|p|) + . +\end{equation} +We write this as a quadratic equation for $\hat u$, and solve it, keeping the solution that decays as $|k|\to\infty$: +\begin{equation} + \rho\hat u(|k|)= + \frac{A_K(|k|)}{A_L(|k|)}\left(\xi(|k|)+1-\sqrt{(\xi(|k|)+1)^2-\frac{A_L(|k|)}{A_K^2(|k|)}\hat S(|k|)}\right) +\end{equation} +that is, +\begin{equation} + \rho\hat u(|k|)= + \frac{\hat S(|k|)}{2A_K(|k|)(\xi(|k|)+1)}\Phi\left({\textstyle\frac{A_L(|k|)}{A_K^2(|k|)}\frac{\hat S(|k|)}{(\xi(|k|)+1)^2}}\right) + \label{uk_easyeq} +\end{equation} +with +\begin{equation} + \xi(|k|):=\frac{k^2}{2\rho\hat A_K} + ,\quad + \Phi(x):=\frac{2(1-\sqrt{1-x})}x + . +\end{equation} +Furthermore, using bipolar coordinates (see lemma\-~\ref{lemma:bipolar}), we write $\hat S$ as +\begin{equation} + \hat S(|k|)=\hat v(|k|)-\frac1{8\pi^3}\int_0^\infty dt\ t\hat u(t)\Eta(|k|,t) + ,\quad + \Eta(y,t):=\frac{2\pi}y\int_{|y-t|}^{y+t}ds\ s\hat v(s) + . + \label{easyeq_Eta} +\end{equation} +By a simple change of variables, +\begin{equation} + \Eta(y,t)= + 4\pi\left(\mathds 1_{y>t}\frac{t}y+\mathds 1_{y\leqslant t}\right) + \int_0^1 ds\ ((y+t)s+|y-t|(1-s))v((y+t)s+|y-t|(1-s)) + . + \label{Eta} +\end{equation} +\bigskip + +\point{\bf Evaluating integrals.} +To compute these integrals numerically, we will use Gauss-Legendre quadratures: +\begin{equation} + \int_0^1 dt\ f(t)\approx\frac12\sum_{i=1}^N w_if\left({\textstyle\frac{r_i+1}2}\right) +\end{equation} +where $w_i$ and $r_i$ are the Gauss-Legendre {\it weights} and {\it abcissa}. +The order $N$ corresponds to the parameter \refname{param:easyeq_order}{{\tt order}}. +The error made by the quadrature is estimated in appendix\-~\ref{appGL}. +We compactify the integrals to the interval $(0,1)$: $y:=\frac1{t+1}$: +\begin{equation} + \hat S(|k|)=\hat v(|k|)-\frac1{8\pi^3}\int_0^1 dy\ \frac{(1-y)\hat u(\frac{1-y}y)\Eta(|k|,\frac{1-y}y)}{y^3} +\end{equation} +so, using the Gauss-Legendre quadrature, we approximate +\begin{equation} + \hat S(|k|)\approx\hat v(|k|)-\frac1{16\pi^3}\sum_{j=1}^N w_j \frac{(1-y_j)\hat u(\frac{1-y_j}{y_j})\Eta(|k|,\frac{1-y_j}{y_j})}{y_j^3} + ,\quad + y_j:=\frac{r_j+1}2 + . +\end{equation} +This suggests a natural discretization of Fourier space: let +\begin{equation} + k_i:=\frac{1-r_i}{1+r_i}\equiv\frac{1-y_i}{y_i} + . + \label{easyeq_ki} +\end{equation} +Thus, defining +\begin{equation} + \mathbb U_i:=\rho\hat u(k_i) + ,\quad + \hat v_i:=\hat v(k_i) +\end{equation} +and approximate\-~(\ref{uk_easyeq}): +\begin{equation} + \mathbb U_i=\frac{\mathbb T_i}{2(\mathbb X_i+1)}\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right) + \label{bbU_easyeq} +\end{equation} +with +\begin{equation} + \mathbb A_{K,i}:=\beta_K\mathbb S_i+(1-\beta_K)\mathbb E + ,\quad + \mathbb B_{i}:=\frac{\beta_L\mathbb S_i+(1-\beta_L)\mathbb E}{\mathbb A_{K,i}} + ,\quad + \mathbb T_i:=\frac{\mathbb S_i}{\mathbb A_{K,i}} +\end{equation} +\begin{equation} + \mathbb S_i:=\hat v_i-\frac1{16\pi^3\rho}\sum_{j=1}^N w_j\frac{(1-y_j)\mathbb U_j\Eta(k_i,k_j)}{y_j^3} + ,\quad + \mathbb E:=\hat v(0)-\frac1{16\pi^3\rho}\sum_{j=1}^N w_j\frac{(1-y_j)\mathbb U_j\Eta(0,k_j)}{y_j^3} +\end{equation} +\begin{equation} + \mathbb X_i:=\frac{k_i^2}{2\rho\mathbb A_{K,i}} + . +\end{equation} +This is a discrete equation for the vector $(\mathbb U_i)_{i=1}^N$. +\bigskip + +\point{\bf Main algorithm to compute $\mathbb U$.}\par\penalty10000 +\medskip\penalty10000 +\subpoint +We rewrite\-~(\ref{bbU_easyeq}) as a root finding problem: +\begin{equation} + \Xi_i(\mathbb U):=\mathbb U_i-\frac{\mathbb T_i}{2(\mathbb X_i+1)}\Phi\left({\textstyle B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right) + =0 + \label{root_easyeq} +\end{equation} +which we solve using the Newton algorithm, that is, we define a sequence of $\mathbb U$'s: +\begin{equation} + \mathbb U^{(n+1)}=\mathbb U^{(n)}-(D\Xi(\mathbb U^{(n)}))^{-1}\Xi(\mathbb U^{(n)}) +\end{equation} +where $D\Xi$ is the Jacobian of $\Xi$: +\begin{equation} + (D\Xi)_{i,j}:=\frac{\partial\Xi_i}{\partial\mathbb U_j} + . +\end{equation} +\bigskip + +\subpoint +For small values of $\rho$, we initialize the algorithm with the hardcore scattering solution +\begin{equation} + \hat u_0(k)=\frac{4\pi a_0}{k^2} + \label{easyeq_init} +\end{equation} +where $a_0$ is the scattering length of the potential $v$ (or an approximation thereof, which need not be very good). +Thus, +\begin{equation} + \mathbb U^{(0)}_i=\frac{4\pi a_0}{k_i^2} + . +\end{equation} +This is a good approximation for small $\rho$. +For larger $\rho$, we choose $\mathbb U^{(0)}$ as the solution of {\tt easyeq} for a slightly smaller $\rho$, and proceed inductively (using the parameters \refname{param:easyeq_minlrho_init}{{\tt minlrho\_init}} and \refname{param:easyeq_nlrho_init}{{\tt nlrho\_init}}). +\bigskip + +\subpoint +We are left with computing the Jacobian of $\Xi$: +\begin{equation} + \begin{largearray} + \frac{\partial\Xi_i}{\partial\mathbb U_j} + =\delta_{j,i} + -\frac1{2(\mathbb X_i+1)}\left( + \partial_j\mathbb T_i-\frac{\mathbb T_i\partial_j\mathbb X_i}{\mathbb X_i+1} + \right)\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right) + \\[0.5cm]\hfill + -\frac{\mathbb T_i}{2(\mathbb X_i+1)^3}\left( + \mathbb B_i\partial_j\mathbb T_i+\mathbb T_i\partial_j\mathbb B_i-2\frac{\mathbb B_i\mathbb T_i\partial_j\mathbb X_i}{\mathbb X_i+1} + \right)\partial\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right) + \end{largearray} + \label{jacobian_easyeq} +\end{equation} +with +\begin{equation} + \partial_j\mathbb B_i= + (\beta_L(1-\beta_K)-\beta_K(1-\beta_L)) + \frac{\mathbb E\partial_j\mathbb S_i-\mathbb S_i\partial_j\mathbb E}{(\beta_K\mathbb S_i+(1-\beta_K)\mathbb E)^2} +\end{equation} +\begin{equation} + \partial_j\mathbb T_i= + (1-\beta_K)\frac{\mathbb E\partial_j\mathbb S_i-\mathbb S_i\partial_j\mathbb E}{(\beta_K\mathbb S_i+(1-\beta_K)\mathbb E)^2} + ,\quad + \partial_j\mathbb A_{K,i}=\beta_K\partial_j\mathbb S_i+(1-\beta_K)\partial_j\mathbb E +\end{equation} +\begin{equation} + \partial_j\mathbb S_i:=-\frac1{16\pi^3\rho}w_j\frac{(1-y_j)\Eta(k_i,k_j)}{y_j^3} + ,\quad + \partial_j\mathbb E:=-\frac1{16\pi^3\rho}\sum_{j=1}^N w_j\frac{(1-y_j)\Eta(0,k_j)}{y_j^3} +\end{equation} +\begin{equation} + \partial_j\mathbb X_i:=-\frac{k_i^2}{2\rho\mathbb A_{K,i}^2}\partial_j\mathbb A_{K,i} + . +\end{equation} +\bigskip + +\subpoint +We iterate the Newton algorithm until the Newton relative error $\epsilon$ becomes smaller than the \refname{param:easyeq_tolerance}{{\tt tolerance}} parameter. +The Newton error is defined as +\begin{equation} + \epsilon=\frac{\|\mathbb U^{(n+1)}-\mathbb U^{(n)}\|_2}{\|\mathbb U^{(n)}\|_2} +\end{equation} +where $\|\cdot\|_2$ is the $l_2$ norm. +The energy thus obtained is +\begin{equation} + e=\frac\rho2\mathds E +\end{equation} +the Fourier transform $\hat u$ of the solution is +\begin{equation} + \hat u(k_i)\approx\frac{\mathbb U_i}\rho +\end{equation} +and the solution $u$ in real space is obtained by inverting the Fourier transform +\begin{equation} + \begin{array}{>\displaystyle r@{\ }>\displaystyle l} + u(|x|)=\int\frac{dk}{8\pi^3}\ e^{-ikx}\hat u(|k|) + =&\frac1{2\pi^2|x|}\int_0^1dy\ \frac{(1-y)\sin(|x|\frac{1-y}y)\hat u(\frac{1-y}y)}{y^3} + \\[0.5cm] + \approx& + \frac1{4\pi^2|x|}\sum_{j=1}^Nw_j (1+k_j)^2k_j\sin(|x|k_j)\hat u(k_j) + . + \end{array} + \label{ux} +\end{equation} +To compute $2u-\rho u\ast u$, we replace $\hat u$ with $2\hat u-\rho\hat u^2$ in the previous equation. +\bigskip + +\point{\bf Condensate fraction.} +Finally, to compute the uncondensed fraction, we solve the modified {\tt easyeq} (see\-~\cite{CJL20b}) +\begin{equation} + (-\Delta+2\mu)u_\mu=v(1-u_\mu)-2\rho K+\rho^2L + \label{easyeq_mu} +\end{equation} +where $K$ and $L$ are defined as in\-~(\ref{easyeq_KL})-(\ref{easyeq_Se}) in which $u$ is replaced with $u_\mu$. +The uncondensed fraction is then +\begin{equation} + \eta=\partial_\mu e|_{\mu=0} + =-\frac\rho2\int dx\ v(|x|)\partial_\mu u_\mu(|x|)|_{\mu=0} + . +\end{equation} +To compute the energy in the presence of the parameter $\mu$, we proceed in the same way as for $\mu=0$, the only difference being that $k^2$ should formally be replaced by $k^2+2\mu$. +In other words, we consider $\mathbb U_i=u_\mu(|k_i|)$ and define $\Xi(\mathbb U,\mu)$ in the same way as in\-~(\ref{root_easyeq}), except that $\mathbb X_i$ should be replaced by +\begin{equation} + \frac{k_i^2+2\mu}{2\rho\mathbb A_{K,i}} + . +\end{equation} +We then solve +\begin{equation} + \Xi(\mathbb U,\mu)=0 + . +\end{equation} +By differentiating this identity with respect to $\mu$, we find $\partial_\mu u_\mu$: +\begin{equation} + D\Xi\partial_\mu \mathbb U=-\partial_\mu\Xi +\end{equation} +and the uncondensed fraction is +\begin{equation} + \eta + =\frac12\int dx\ v(x)(D\Xi)^{-1}\partial_\mu\Xi|_{\mu=0} +\end{equation} +thus +\begin{equation} + \eta=\frac1{16\pi^3}\int_0^1 dy\ \frac{(1-y)\Eta(0,\frac{1-y}y)(D\Xi)^{-1}\partial_\mu\Xi|_{\mu=0}(\frac{1-y}y)}{y^3} +\end{equation} +which we approximate using a Gauss-Legendre quadrature: +\begin{equation} + \eta\approx\frac1{32\pi^3}\sum_{j=1}^N w_j(1-k_j)^2k_j\Eta(0,k_j)\sum_{i=1}^N(D\Xi)^{-1}_{j,i}\partial_\mu\Xi_i|_{\mu=0} + . + \label{eta_easyeq} +\end{equation} +We then compute, using\-~(\ref{jacobian_easyeq}), +\begin{equation} + \partial_\mu\Xi_i|_{\mu=0} + = + \frac{\mathbb T_i}{2\rho\mathbb A_{K,i}(\mathbb X_i+1)^2}\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right) + +\frac{\mathbb B_i\mathbb T_i^2}{\rho\mathbb A_{K,i}(\mathbb X_i+1)^4}\partial\Phi\left({\textstyle\mathbb B_i\frac{\mathbb T_i}{(\mathbb X_i+1)^2}}\right) + . +\end{equation} + +\subsection{\tt anyeq}\label{sec:anyeq} +\indent +This method is used to solve any of the equations in the Simplified approach. +Specifically, it solves the equation +\begin{equation} + -\Delta u + = + v(1-u)-2\rho K+\rho^2 L + \label{anyeq} +\end{equation} +with +\begin{equation} + K=\gamma_K(1-\alpha_Ku)\left(\beta_K u\ast S+(1-\beta_K)\frac{2e}\rho u\right) + \label{anyeq_K} +\end{equation} +and +\begin{equation} + L:=L_1+L_2+L_3 +\end{equation} +\begin{equation} + L_1:=(1-\alpha_{L,1}u)\left(\beta_{L,1}u\ast u\ast S+(1-\beta_{L,1})\frac{2e}\rho u\ast u\right) +\end{equation} +\begin{equation} + L_2:=-\gamma_{L,2}(1-\alpha_{L,2}u)\left(\beta_{L,2}2u\ast(u(u\ast S))+(1-\beta_{L,2})\frac{4e}\rho u\ast u^2\right) +\end{equation} +\begin{equation} + L_3:=\gamma_{L,3}(1-\alpha_{L,3}u)\left(\beta_{L,3}\frac12\int dydz\ u(y)u(z-x)u(z)u(y-x)S(z-y)+(1-\beta_{L,3})\frac e\rho u^2\ast u^2\right) +\end{equation} +\begin{equation} + e=\frac\rho2\int dx\ (1-u(|x|))v(|x|) + . + \label{anyeq_e} +\end{equation} +The parameters $\alpha_\cdot$, $\beta_\cdot$ and $\gamma_\cdot$ can be set to turn\-~(\ref{anyeq}) into any of the approximations of the Simplified approach. +For ease of use, there are several predefined equations, given in the following table. +\bigskip + +\def\arraystretch{1.5} +\setlength\tabcolsep{5pt} +\makelink{table:anyeq_eqs}{} +\hfil\begin{tabular}{|r|c|c|c|c|c|c|c|c|c|c|c|c|} + \hline + &$\alpha_K$ &$\beta_K$ &$\gamma_K$ &$\alpha_{L,1}$ &$\beta_{L,1}$ &$\alpha_{L,2}$ &$\beta_{L,2}$ &$\gamma_{L,2}$ &$\alpha_{L,3}$ &$\beta_{L,3}$ &$\gamma_{L,3}$\\ + \hline + \makelink{eq:anyeq_compleq}{}{\tt compleq}&1&1&1&1&1&1&1&1&1&1&1\\ + \makelink{eq:anyeq_bigeq}{} {\tt bigeq }&1&1&1&1&1&1&1&1&-&-&0\\ + \makelink{eq:anyeq_fulleq}{} {\tt fulleq }&1&1&1&1&1&1&1&1&1&0&1\\ + \makelink{eq:anyeq_medeq}{} {\tt medeq }&0&1&1&0&1&-&-&0&-&-&0\\ + \makelink{eq:anyeq_simpleq}{}{\tt simpleq}&0&0&1&0&0&-&-&0&-&-&0\\ + \hline +\end{tabular} +\bigskip + +Note that there is no $\gamma_{L,1}$, whose computation would be rather different. +Note, in addition, that {\tt simpleq} and {\tt medeq} coincide with their definitions in\-~(\ref{easyeq}). +The method used to solve this equation is very different from \refname{sec:easyeq}{{\tt easyeq}}, and is significantly longer to run. +\bigskip + +\subsubsection{Usage} +\indent +Unless otherwise noted, this method takes the following parameters (specified via the {\tt [-p params]} flag, as a `{\tt;}' separated list of entries). +\begin{itemize} + \item\makelink{param:anyeq_rho}{} + {\tt rho} ({\tt Float64}, default: $10^{-6}$): density $\rho$. + + \item\makelink{param:anyeq_tolerance}{} + {\tt tolerance} ({\tt Float64}, default: $10^{-11}$): maximal size of final step in Newton iteration. + + \item\makelink{param:anyeq_maxiter}{} + {\tt maxiter} ({\tt Int64}, default: 21): maximal number of iterations of the Newton algorithm before giving up. + + \item\makelink{param:anyeq_P}{} + {\tt P} ({\tt Int64}, default: 11): order of all Chebyshev polynomial expansions (denoted by $P$ below). + + \item\makelink{param:anyeq_N}{} + {\tt N} ({\tt Int64}, default: 12): order of all Gauss quadratures (denoted by $N$ below). + + \item\makelink{param:anyeq_J}{} + {\tt J} ({\tt Int64}, default: 10): number of splines (denoted by $J$ below). + + \item\makelink{param:anyeq_minlrho_init}{} + {\tt minlrho\_init} ({\tt Float64}, default: \refname{param:anyeq_rho}{{\tt rho}}): we initialize the Newton algorithm using the solution of \refname{eq:easyeq_medeq}{{\tt medeq}}, computed using the methods in \refname{sec:easyeq}{{\tt easyeq}}. This option is passed to the underlying \refname{sec:easyeq}{{\tt easyeq}} routine. + + \item\makelink{param:anyeq_nlrho_init}{} + {\tt nlrho\_init} ({\tt Int64}, default: 1): this option is passed to the underlying \refname{sec:easyeq}{{\tt easyeq}} routine to initialize the Newton algorithm. + + \item\makelink{param:anyeq_aK}{} + {\tt aK}, {\tt bK}, {\tt gK}, {\tt aL1}, {\tt bL1}, {\tt aL2}, {\tt bL2}, {\tt gL2}, {\tt aL3}, {\tt bL3}, {\tt gL3} ({\tt Float64}, default: 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0): the values of $\alpha_K$, $\beta_K$, $\gamma_K$, $\alpha_{L,1}$, $\beta_{L,1}$, $\alpha_{L,2}$, $\beta_{L,2}$, $\gamma_{L,2}$, $\alpha_{L,3}$, $\beta_{L,3}$, $\gamma_{L,3}$. + + \item\makelink{param:anyeq_eq}{} + {\tt eq} ({\tt String}, default: ``{\tt bigeq}'', acceptable values: ``{\tt compleq}'', ``{\tt bigeq}'', ``{\tt fulleq}'', ``{\tt medeq}'', ``{\tt simpleq}''): A shortcut to select any of the equations defined in the \refname{table:anyeq_eqs}{table above}. When this option is set, none of + \refname{param:anyeq_aK}{\tt aK}, \refname{param:anyeq_aK}{\tt bK}, \refname{param:anyeq_aK}{\tt gK}, \refname{param:anyeq_aK}{\tt aL1}, \refname{param:anyeq_aK}{\tt bL1}, \refname{param:anyeq_aK}{\tt aL2}, \refname{param:anyeq_aK}{\tt bL2}, \refname{param:anyeq_aK}{\tt gL2}, \refname{param:anyeq_aK}{\tt aL3}, \refname{param:anyeq_aK}{\tt bL3}, \refname{param:anyeq_aK}{\tt gL3} should be set. +\end{itemize} +\bigskip + +The available {\tt commands} are the following. + +\begin{itemize} + \item\makelink{command:anyeq_energy}{} + {\tt energy}: compute the energy $e$ at a given $\rho$.\par + \underline{Output}: [$e$] [Newton error $\epsilon$]. + + \item\makelink{command:anyeq_energy_rho}{} + {\tt energy\_rho}: compute the energy $e$ as a function of $\rho$. + The Newton algorithm is initialized with the solution of \refname{eq:easyeq_medeq}{{\tt medeq}}.\par + \underline{Disabled parameters}: \refname{param:anyeq_rho}{{\tt rho}}, \refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}} and \refname{param:anyeq_nlrho_init}{{\tt nlrho\_init}}.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:anyeq_minlrho}{} + {\tt minlrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\log_{10}\rho$. + + \item\makelink{param:anyeq_maxlrho}{} + {\tt maxlrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\log_{10}\rho$. + + \item\makelink{param:anyeq_nlrho}{} + {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically). + + \item\makelink{param:anyeq_rhos}{} + {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list. + \end{itemize} + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$].\par + \underline{Multithread support}: yes, different values of $\rho$ split up among workers. + + \item\makelink{command:anyeq_energy_rho_init_prevrho}{} + {\tt energy\_rho\_init\_prevrho}: compute the energy $e$ as a function of $\rho$. + The Newton algorithm is initialized with the solution of \refname{eq:easyeq_medeq}{{\tt medeq}} for the lowest $\rho$, and with the previously computed $\rho$ for the larger densities.\par + \underline{Disabled parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Extra parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$]. + + \item\makelink{command:anyeq_energy_rho_init_nextrho}{} + {\tt energy\_rho\_init\_nextrho}: same as \refname{command:anyeq_energy_rho_init_prevrho}{{\tt energy\_rho\_init\_prevrho}} except that the energy is computed for decreasing densities instead of increasing ones. + The Newton algorithm is initialized with the solution of \refname{eq:easyeq_medeq}{{\tt medeq}} for the largest $\rho$, and with the previously computed $\rho$ for the smaller densities.\par + \underline{Disabled parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Extra parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$]. + + \item\makelink{command:anyeq_condensate_fraction}{} + {\tt condensate\_fraction}: compute the uncondensed fraction $\eta$ at a given $\rho$.\par + \underline{Output}: [$\eta$] [Newton error $\epsilon$]. + + \item\makelink{command:anyeq_condensate_fraction_rho}{} + {\tt condensate\_fraction\_rho}: compute the uncondensed fraction $\eta$ as a function of $\rho$. + The Newton algorithm is initialized with the solution of \refname{eq:easyeq_medeq}{{\tt medeq}}.\par + \underline{Disabled parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Extra parameters}: same as \refname{command:anyeq_energy_rho}{{\tt energy\_rho}}.\par + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$\eta$] [Newton error $\epsilon$].\par + \underline{Multithread support}: yes, different values of $\rho$ split up among workers. + + \item\makelink{command:anyeq_uk}{} + {\tt uk}: compute the Fourier transform $\hat u(|k|)$. + The values $|k|$ at which $\hat u$ is computed are those coming from the Gauss quadratures, and cannot be set.\par + \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\hat u(|k|)$] + + \item\makelink{command:anyeq_ux}{} + {\tt ux}: compute $u$ as a function of $|x|$.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:anyeq_xmin}{} + {\tt xmin} ({\tt Float64}, default: 0): minimum of the range of $|x|$ to be printed. + + \item\makelink{param:anyeq_xmax}{} + {\tt xmax} ({\tt Float64}, default: 100): maximum of the range of $|x|$ to be printed. + + \item\makelink{param:anyeq_nx}{} + {\tt nx} ({\tt Int64}, default: 100): number of points to print (linearly spaced). + + \end{itemize} + \underline{Output} (one line for each value of $x$): [$|x|$] [$\mathcal Re(u(|x|))$] [$\mathcal Im(u(|x|))$] + + \item\makelink{command:anyeq_momentum_distribution}{} + {\tt momentum\_distribution}: compute the momentum distribution $\mathfrak M(|k|)$ at a given $\rho$.\par + \underline{Output} (one line for each value of $|k|$): [$|k|$] [$\mathfrak M(|k|)$] + + \item\makelink{command_anyeq_2pt}{} + {\tt 2pt}: compute the two-point correlation function $C_2(|x|)$ at a given $\rho$.\par + \underline{Extra parameters}: same as \refname{command:anyeq_ux}{{\tt ux}}, plus\par + \begin{itemize} + \item\makelink{param:anyeq_window_L}{} + {\tt window\_L} ({\tt Float64}, default: $10^3$): size of the Hann window used to numerically invert the Fourier transform in the computation of the tow-point correlation function, see\-~(\ref{hann}). + \end{itemize} + \underline{Output} (one line for each value of $|x|$): [$|x|$] [$C_2(|x|)$] + + \item \makelink{command:anyeq_save_Abar}{} + {\tt save\_Abar}: compute the matrix $\bar A$. This matrix is used to compute observables for \refname{eq:anyeq_compleq}{{\tt compleq}}. This command is useful to output the value of $\bar A$ to a file once and for all, and use this file to run commands without recomputing $\bar A$.\par + \underline{Disabled parameters}: \refname{param:anyeq_rho}{{\tt rho}}, \refname{param:anyeq_tolerance}{{\tt tolerance}}, \refname{param:anyeq_maxiter}{{\tt maxiter}}, \refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}}, \refname{param:anyeq_nlrho}{{\tt nlrho}}.\par + \underline{Output}: [$\bar A$] (the output is not designed to be human-readable; it is obtained through nested {\tt for} loops; for details, see the code).\par + \underline{Multithread support}: yes, the first indices are split up among workers, which produces $NJ$ jobs. + +\end{itemize} + +\subsubsection{Description} +\point{\bf Fourier space formulation.} +The computation is carried out in Fourier space. +We take the convention that the Fourier transform of a function $f(|x|)$ is +\begin{equation} + \hat f(|k|)=\int_{\mathbb R^3} dx\ e^{ikx}f(|x|) + =\frac{4\pi}{|k|}\int_0^\infty dr\ \frac{\sin(|k|r)}rf(r) + . + \label{anyeq_fourier} +\end{equation} +We define a Fourier-space convolution: +\begin{equation} + \hat f\hat\ast\hat g(|k|):=\int_{\mathbb R^3}\frac{dp}{(2\pi)^3}\ \hat f(|k-p|)\hat g(|p|) + . + \label{anyeq_hatast} +\end{equation} +In Fourier space, (\ref{anyeq}) becomes +\begin{equation} + k^2\hat u(|k|)=\hat S(|k|)-2\rho\hat K(|k|)+\rho^2\hat L(|k|) +\end{equation} +with +\begin{equation} + \hat S(|k|)=\int_{\mathbb R^3} dx\ e^{ikx}(1-u(|x|))v(|x|) + =\hat v(k)-\hat u\hat\ast\hat v(k) +\end{equation} +\begin{equation} + \rho\hat K= + \gamma_K(\beta_K\rho\hat S+(1-\beta_K)2e)\hat u + - + \gamma_K\alpha_K\hat u\hat\ast((\beta_K\rho\hat S+(1-\beta_K)2e)\hat u) +\end{equation} +\begin{equation} + \rho\hat L_1= + (\beta_{L,1}\rho\hat S+(1-\beta_{L,1})2e)\hat u^2 + - + \alpha_{L,1}\hat u\hat\ast((\beta_{L,1}\rho\hat S+(1-\beta_{L,1})2e)\hat u^2) +\end{equation} +\begin{equation} + \rho\hat L_2= + -\gamma_{L,2}(\beta_{L,2}2\rho(\hat u\hat\ast(\hat u\hat S))+(1-\beta_{L,2})4e\hat u\hat\ast\hat u)\hat u + + + \gamma_{L,2}\alpha_{L,2}\hat u\hat\ast((\beta_{L,2}2\rho(\hat u\hat\ast(\hat u\hat S))+(1-\beta_{L,2})4e\hat u\hat\ast\hat u)\hat u) +\end{equation} +\begin{equation} + \rho\hat L_3= + \gamma_{L,3}(1-\beta_{L,3})e(\hat u\hat\ast\hat u)^2 + - + \gamma_{L,3}\alpha_{L,3}(1-\beta_{L,3})\hat u\hat\ast(e(\hat u\hat\ast\hat u)^2) + + + \gamma_{L,3}\beta_{L,3}\hat l_3 + - + \frac1{2\rho}\gamma_{L,3}\alpha_{L,3}\beta_{L,3}\hat u\hat\ast\hat l_3 +\end{equation} +with +\begin{equation} + \hat l_3(|k|):= + \frac1{\rho^2}\int\frac{dq}{(2\pi)^3}\frac{dq'}{(2\pi)^3}\ \rho\hat u(|k-q'|)\rho\hat u(|q|)\rho\hat u(|q'|)\rho\hat u(|k-q|)\hat S(|q'-q|) + \label{l3} +\end{equation} +Therefore, +\begin{equation} + \rho^2\hat u(|k|)^2\sigma_{L,1}(|k|) + -\left(\frac{k^2}\rho+2\sigma_K(|k|)+2f_1(|k|)\right)\rho\hat u(|k|) + +\left(\hat S(|k|)+\frac12f_2(|k|)+G(|k|)\right) + = + 0 +\end{equation} +with +\begin{equation} + \sigma_K:=\frac1{\rho}\gamma_K(\beta_K\rho\hat S+(1-\beta_K)2e) + ,\quad + \sigma_{L,1}:=\frac1{\rho}(\beta_{L,1}\rho\hat S+(1-\beta_{L,1})2e) +\end{equation} +\begin{equation} + f_1:=\frac1{\rho^2}\gamma_{L,2}(\beta_{L,2}\rho(\rho\hat u\hat\ast(\rho\hat u\hat S))+(1-\beta_{L,2})2e\rho\hat u\hat\ast\rho\hat u) +\end{equation} +\begin{equation} + f_2:=\gamma_{L,3}(1-\beta_{L,3})\frac{2e}{\rho^3}(\rho\hat u\hat\ast\rho\hat u)^2 + +\gamma_{L,3}\beta_{L,3}\hat l_3 +\end{equation} +\begin{equation} + \begin{largearray} + G:= + \frac2{\rho^2}\gamma_K\alpha_K\rho\hat u\hat\ast((\beta_K\rho\hat S+(1-\beta_K)2e)\rho\hat u) + - + \frac1{\rho^2}\alpha_{L,1}\rho\hat u\hat\ast((\beta_{L,1}\rho\hat S+(1-\beta_{L,1})2e)\rho\hat u^2) + \\[0.5cm]\hfill + + + \frac2{\rho}\alpha_{L,2}\rho\hat u\hat\ast(f_1\rho\hat u) + - + \frac1{2\rho}\alpha_{L,3}\rho\hat u\hat\ast f_2 + . + \end{largearray} +\end{equation} +Therefore, +\begin{equation} + \rho\hat u=1+\xi + -\sqrt{(1+\xi)^2-(1+\zeta)} + \label{u} +\end{equation} +with +\begin{equation} + \xi:=\frac1{\sigma_{L,1}}\left(\sigma_K-\sigma_{L,1}+\frac{k^2}{2\rho}+f_1\right) + ,\quad + \zeta:=\frac1{\sigma_{L,1}}\left(\hat S-\sigma_{L,1}+\frac12f_2+G\right) +\end{equation} +We rewrite\-~(\ref{u}) as +\begin{equation} + U= + \frac{1+\zeta}{2(\xi+1)}\ \Phi\left({\textstyle\frac{1+\zeta}{(\xi+1)^2}}\right) + \label{anyeq_hatu} +\end{equation} +with +\begin{equation} + U:=\rho\hat u +\end{equation} +\begin{equation} + \Phi(x):=\frac{2(1-\sqrt{1-x})}x + . +\end{equation} +\bigskip + +\point{\bf Evaluating integrals.} +To evaluate integrals numerically, we will split integration intervals over splines and use Gauss-Legendre quadratures. +More specifically, to compute integrals of the form +\begin{equation} + \int \frac{dk}{(2\pi)^3}\ f(U(k),k) + = + \frac1{2\pi^2}\int_0^\infty dt\ t^2f(U(t),t) +\end{equation} +we first compactify space to $(-1,1]$ by changing variables to $\tau=\frac{1-t}{1+t}$: +\begin{equation} + \int \frac{dk}{(2\pi)^3}\ f(U(k),k) + = + \frac1{\pi^2}\int_{-1}^1 d\tau\ \frac{(1-\tau)^2}{(1+\tau)^4}f({\textstyle U(\frac{1-\tau}{1+\tau}),\frac{1-\tau}{1+\tau}}) + . +\end{equation} +We then split $(-1,1]$ into $J$ sub-intervals (given by the parameter \refname{param:anyeq_J}{{\tt J}}), called {\it splines}: +\begin{equation} + (-1,1]=\bigcup_{j=0}^{J-1}(\tau_j,\tau_{j+1}] + . +\end{equation} +The $\tau$ are taken to be equally spaced, but the code is designed in such a way that this could be changed easily in the future: +\begin{equation} + \tau_j=-1+\frac{2j}J + . +\end{equation} +In these terms, +\begin{equation} + \int \frac{dk}{(2\pi)^3}\ f(U(k),k) + = + \sum_{l=0}^{J-1} + \int_{\tau_l}^{\tau_{l+1}} d\tau\ \frac{(1-\tau)^2}{(1+\tau^4}g(U({\textstyle\frac{1-\tau}{1+\tau},\frac{1-\tau}{1+\tau}}) + . +\end{equation} +We then change variables to $r$: +\begin{equation} + \tau= + -\frac{\tau_{l+1}-\tau_l}2\sin({\textstyle\frac{\pi r}2})+\frac{\tau_{l+1}+\tau_l}2 + =:\frac{1-\vartheta_l(r)}{1+\vartheta_l(r)} + \label{chebyshevvars} +\end{equation} +(the reason for this specific change of variables will become clear at the end of this paragraph and in the next one) +and find +\begin{equation} + \int \frac{dk}{(2\pi)^3}\ f(U(k),k) + = + \sum_{l=0}^{J-1} + \frac{\tau_{l+1}-\tau_l}{16\pi}\int_{-1}^1 dr\ \cos({\textstyle\frac{\pi r}2})(1+\vartheta_l(r))^2\vartheta_l^2(r)f(U(\vartheta_l(r)),\vartheta_l(r)) + . +\end{equation} +We then approximate the integral using a Gauss-Legendre quadrature of order $N$ (see appendix\-~\ref{appGL}): +\begin{equation} + \int \frac{dk}{(2\pi)^3}\ f(k) + \approx + \sum_{l=0}^{J-1} + \frac{\tau_{l+1}-\tau_l}{16\pi} + \sum_{j=1}^Nw_j + \cos({\textstyle\frac{\pi x_j}2})(1+\vartheta_l(x_j))^2\vartheta_l^2(x_j)f(\mathbb U_{l,j},\vartheta_l(x_j)) + \label{anyeq_integration} +\end{equation} +with +\begin{equation} + \mathbb U_{l,j}:=U(k_{l,j}) + ,\quad + k_{l,j}:=\frac{2+(\tau_{l+1}-\tau_l)\sin(\frac{\pi x_j}2)-(\tau_{l+1}+\tau_\alpha)}{2-(\tau_{l+1}-\tau_l)\sin(\frac{\pi x_j}2)+(\tau_{l+1}+\tau_l)} + . +\end{equation} +The choice of the change of variables\-~(\ref{chebyshevvars}) is made so that $U$ is evaluated at $k_{l,j}$, which are the momenta that appear naturally in the Chebyshev polynomial expansion below\-~(\ref{klj}). +In this way, we can compute arbitrary integrals of functions of $U$ by just computing the values of $\mathbb U_{l,j}$. +\bigskip + +\point{\bf Chebyshev polynomial expansion.} +In the case of \refname{sec:easyeq}{{\tt easyeq}}, we saw that using Gauss quadratures reduced the computation to evaluating $U$ at a fixed set of discrete momenta. +This is not the case here, due to the presence of more complicated convolutions of $U$. +Instead, we will approximate $U$ using polynomial approximations. +We will now discuss this approximation for an arbitrary function $a(|k|)$, as we will need it for other functions than simply $U$. +\bigskip + +\subpoint +As in the previous paragraph, we compactify $[0,\infty)$ to $(-1,1]$ via the change of variables $r\mapsto\frac{1-r}{1+r}$, and split the interval into splines. +Inside each spline, we use a Chebyshev polynomial expansion. +However, we need to be careful in the first spline, which encapsulates the behavior at $|k|\to\infty$: $U$ decays to 0 as $|k|\to\infty$, and this behavior cannot be reproduced by a polynomial expansion. +To avoid this, if $a\sim|k|^{-\nu_a}$, we expand $|k|^{\nu_a}a$ instead of $a$. +Actually, to simplify expressions in the presence of the compactification, we will expand $2^{-\nu_a}(1+|k|)^{\nu_a}a$, which makes no conceptual difference. +In the case of $a=U$, we will use $\nu=2$, which we know holds for the Simple equation\-~\cite{CJL20}. +Putting all this together, we write, for $\tau\in(-1,1]$, if $|k|\equiv\frac{1-\tau}{1+\tau}$, +\begin{equation} + 2^{-\nu_a}(1+|k|)^{\nu_a}a(|k|)\equiv + \frac1{(1+\tau)^{\nu_a}}a({\textstyle\frac{1-\tau}{1+\tau}}) + =\sum_{l=0}^{J-1}\mathds 1_{\tau_l<\tau\leqslant\tau_{l+1}}\sum_{n=0}^\infty \mathbf F_{l,n}^{(\nu_a)}(a) T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}}) + \label{a_chebyshev_spline} +\end{equation} +in which $\mathds 1_{\tau_l<\tau\leqslant\tau_{l+1}}\in\{0,1\}$ is equal to 1 if and only if $\tau_l<\tau\leqslant\tau_{l+1}$, and +\begin{equation} + \mathbf F_{l,n}^{(\nu_a)}(a):= + \frac{2-\delta_{n,0}}{\pi} + \int_0^{\pi} d\theta\ + \frac{\cos(n\theta)}{(1+\frac{\tau_{l+1}-\tau_l}2\cos(\theta)+\frac{\tau_{l+1}+\tau_l}2)^{\nu_a}} + a({\textstyle\frac{2-(\tau_{l+1}-\tau_l)\cos(\theta)-(\tau_{l+1}+\tau_\alpha)}{2+(\tau_{l+1}-\tau_l)\cos(\theta)+(\tau_{l+1}+\tau_l)}}) + \label{bfF} +\end{equation} +(see appendix\-~\ref{appChebyshev} for a discussion of the Chebyshev polynomial expansion and the error of its truncations). +\bigskip + +\subpoint +In order to compute an approximation for $a$ using\-~(\ref{a_chebyshev_spline}), we will truncate the sum over $n$ to a finite value $P$ (given by the parameter \refname{param:anyeq_P}{{\tt P}}). +In addition, to compute the integral in\-~(\ref{bfF}), we will use a Gauss-Legendre quadrature of order $N$ (given by the parameter \refname{param:anyeq_N}{{\tt N}}), see appendix\-~\ref{appGL}: +\begin{equation} + \mathbf F_{l,n}^{(\nu_a)}(a)\approx\mathfrak F_{l,n}^{(\nu_a)}(\mathfrak a):= + \frac{2-\delta_{n,0}}2\sum_{j=1}^N w_j \cos({\textstyle\frac{n\pi(1+x_j)}2})\frac{\mathfrak a_{l,j}}{(1-\frac{\tau_{l+1}-\tau_l}2\sin(\frac{\pi x_j}2)+\frac{\tau_{l+1}+\tau_l}2)^{\nu_a}} + \label{frakF} +\end{equation} +with +\begin{equation} + \mathfrak a_{l,j}:=a(k_{l,j}) + ,\quad + k_{l,j}:=\frac{2+(\tau_{l+1}-\tau_l)\sin(\frac{\pi x_j}2)-(\tau_{l+1}+\tau_\alpha)}{2-(\tau_{l+1}-\tau_l)\sin(\frac{\pi x_j}2)+(\tau_{l+1}+\tau_l)} + \label{klj} +\end{equation} +and $(x_j,w_j)$ are the abcissa and weights for Gauss-Legendre quadratures. +\bigskip + +\subpoint +All in all, we approximate +\begin{equation} + a({\textstyle\frac{1-\tau}{1+\tau}}) + \approx(1+\tau)^{\nu_a}\sum_{l=0}^{J-1}\mathds 1_{\tau_l<\tau\leqslant\tau_{l+1}}\sum_{n=0}^P \mathfrak F_{l,n}^{(\nu_a)}(\mathfrak a) T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}}) + \label{approxchebyshev} +\end{equation} +with $\mathfrak F$ defined in\-~(\ref{frakF}). +Furthermore, using the Chebyshev polynomial expansion and Gauss-Legendre quadratures, we can compute all the observables we are interested in by computing $\mathbb U_{l,j}\equiv U(k_{l,j})$. +With this in mind, we will represent the function $U$ as a vector of dimension $NJ$ whose components are $\mathbb U_{l,j}$. +\bigskip + +\point{\bf Convolutions.} +Using the Chebyshev polynomial approximation, we can compute convolutions as follows. +\medskip + +\subpoint +First, we rewrite the convolution as a two-dimensional integral, using bipolar coordinates (see lemma\-~\ref{lemma:bipolar}): +\begin{equation} + (a\hat\ast b)(|k|)=\frac1{4\pi^2|k|}\int_0^\infty dt\ ta(t)\int_{||k|-t|}^{|k|+t}ds\ sb(s) +\end{equation} +We change variables to compactify the integrals $\tau=\frac{1-t}{1+t}$, $\sigma=\frac{1-s}{1+s}$: +\begin{equation} + (a\hat\ast b)(|k|)=\frac1{4\pi^2|k|}\int_{-1}^1 d\tau\ \frac{2(1-\tau)}{(1+\tau)^3}a({\textstyle\frac{1-\tau}{1+\tau}})\int_{\alpha_-(|k|,\tau)}^{\alpha_+(|k|,\tau)}d\sigma\ \frac{2(1-\sigma)}{(1+\sigma)^3}b({\textstyle\frac{1-\sigma}{1+\sigma}}) + \label{convvar} +\end{equation} +with +\begin{equation} + \alpha_-(|k|,\tau):=\frac{1-|k|-\frac{1-\tau}{1+\tau}}{1+|k|+\frac{1-\tau}{1+\tau}} + ,\quad + \alpha_+(|k|,\tau):=\frac{1-||k|-\frac{1-\tau}{1+\tau}|}{1+||k|-\frac{1-\tau}{1+\tau}|} + . + \label{anyeq_alpha} +\end{equation} +Therefore, using the approximation\-~(\ref{approxchebyshev}), if $\mathfrak a_{l,j}:=a(k_{l,j})$ and $\mathfrak b_{l,j}:=b(k_{l,j})$ (\ref{klj}), +\begin{equation} + (a\hat\ast b)(|k_{l,j}|)\approx + (\mathfrak a\odot \mathfrak b)_{l,j}:= + \frac1{4\pi^2|k_{l,j}|} + \sum_{n,m=0}^P + \sum_{l,l'=0}^{J-1} + \mathfrak F_{l,n}^{(\nu_a)}(\mathfrak a)\mathfrak F_{l',m}^{(\nu_b)}(\mathfrak b) + A_{l,n;l',m}^{(\nu_a,\nu_b)}(|k_{l,j}|) + \label{odot} +\end{equation} +with +\begin{equation} + \begin{largearray} + A_{l,n;l',m}^{(\nu_a,\nu_b)}(|k|):= + \int_{\tau_l}^{\tau_{l+1}} d\tau\ \frac{2(1-\tau)}{(1+\tau)^{3-\nu_a}}T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}}) + \cdot\\[0.5cm]\hfill\cdot + \mathds 1_{\alpha_-(|\tilde k|,\tau)<\tau_{\zeta'+1}} + \mathds 1_{\alpha_+(|\tilde k|,\tau)>\tau_{\zeta'}} + \int_{\max(\tau_{l'},\alpha_-(|k|,\tau))}^{\min(\tau_{l'+1},\alpha_+(|k|,\tau))}d\sigma\ \frac{2(1-\sigma)}{(1+\sigma)^{3-\nu_b}}T_m({\textstyle\frac{2\sigma-(\tau_{l'}+\tau_{l'+1})}{\tau_{l'+1}-\tau_{l'}}}) + \end{largearray} +\end{equation} +(we need the indicator functions to ensure that the bounds of the integral are correct). +Note that $A$ is independent of $a$ and $b$, and can be computed once and for all at the beginning of execution for all values of $k_{l,j}$ (\ref{klj}). +We then compute the integrals using Gauss-Legendre quadratures (as is proved in the next paragraph, the integrand is non singular provided $\nu_a,\nu_b\geqslant 2$). +\bigskip + +\subpoint +Note that these integrals are not singular as long as $\nu_a,\nu_b\geqslant 2$: indeed (since the only possible problems occur at $-1$, it suffices to consider the case with only one spline), $\alpha_-,\alpha_+>-1$ for $\tau>-1$, and +\begin{equation} + \alpha_\pm(k,\tau)=-1+(1+\tau)\pm\frac k2(1+\tau)^2+O(1+\tau)^3 +\end{equation} +and +\begin{equation} + \int_{\alpha_-(|k|,\tau)}^{\alpha_+(|k|,\tau)}d\sigma\ \left|\frac{2(1-\sigma)}{(1+\sigma)^{3-\nu_b}}T_m(\sigma)\right| + \leqslant + 4\int_{\alpha_-(|k|,\tau)}^{\alpha_+(|k|,\tau)}d\sigma\ \frac1{(1+\sigma)^{3-\nu_b}} +\end{equation} +which, if $\nu_b=2$, yields +\begin{equation} + 4\log\left(\frac{1+\alpha_+(|k|,\tau)}{1+\alpha_-(|k|,\tau)}\right) + =4|k|(1+\tau)+O(1+\tau)^2 +\end{equation} +and if $\nu_b>2$, yields +\begin{equation} + \frac4{\nu_b-2}\left((1+\alpha_+(|k|,\tau))^{\nu_b-2}-(1+\alpha_-(|k|,\tau))^{\nu_b-2}\right) + = + \frac4{\nu_b-2}|k|(1+\tau)^{\nu_b-1}(1+O(1+\tau)) + . +\end{equation} +Therefore, +\begin{equation} + \left|\frac{2(1-\tau)}{(1+\tau)^{3-\nu_a}}T_n(\tau)\int_{\alpha_-(|k|,\tau)}^{\alpha_+(|k|,\tau)}d\sigma\ \frac{2(1-\sigma)}{(1+\sigma)^{3-\nu_b}}T_m(\sigma)\right| + \leqslant + \frac8{c_{\nu_b}} + |k|(1-\tau)(1+\tau)^{\nu_a+\nu_b-4}(1+O(1+\tau)) +\end{equation} +where $c_{2}:=1$ and $c_{\nu_b}:=\nu_b-2$ if $\nu_b>2$. +The integrand is, therefore, not singular as long as $\nu_a,\nu_b\geqslant 2$. +\bigskip + +\subpoint +Evaluating convolutions at $k=0$ is not immediate, as the formula for $A(0)$ involves a bit of a computation. +To compute $A(0)$, we expand +\begin{equation} + \alpha_-(|k|,\tau)=\tau-\frac{|k|(1+\tau)^2}2+O(k^2) + ,\quad + \alpha_+(|k|,\tau)=\tau+\frac{|k|(1+\tau)^2}2+O(k^2) +\end{equation} +so +\begin{equation} + A_{\zeta,n;\zeta',m}^{(\nu_a,\nu_b)}(0)=\mathds 1_{\zeta=\zeta'} + \frac1{\pi^2} + \int_{\tau_\zeta}^{\tau_{\zeta+1}} d\tau\ \frac{(1-\tau)^2}{(1+\tau)^{4-\nu_a-\nu_b}}T_n({\textstyle\frac{2\tau-(\tau_\zeta+\tau_{\zeta+1})}{\tau_{\zeta+1}-\tau_\zeta}}) + T_m({\textstyle\frac{2\tau-(\tau_{\zeta}+\tau_{\zeta+1})}{\tau_{\zeta+1}-\tau_{\zeta}}}) +\end{equation} +which we then compute using a Gauss-Legendre quadrature. +We can then evaluate convolutions at $k=0$: +\begin{equation} + (a\hat\ast b)(0)\approx\left<\mathfrak a\mathfrak b\right> + := + \frac1{4\pi^2|k_{l,j}|} + \sum_{n,m=0}^P + \sum_{l,l'=0}^{J-1} + \mathfrak F_{l,n}^{(\nu_a)}(\mathfrak a)\mathfrak F_{l',m}^{(\nu_b)}(\mathfrak b) + A_{l,n;l',m}^{(\nu_a,\nu_b)}(0) + \label{avg} +\end{equation} +\bigskip + + +\subpoint +Let us now compute some choices of $\mathfrak a,\mathfrak b$ more explicitly. +\medskip + +\subsubpoint +Let us start with $\mathds 1_{l,n}\hat\ast a$ where $\mathds 1_{l,n}$ is the vector which has 0's everywhere except at position $(l,n)$. +We have +\begin{equation} + \mathfrak F_{l',m}^{(\nu_1)}(\mathds 1_{l,n})=\delta_{l',l}\frac{2-\delta_{m,0}}2w_n\frac{\cos({\textstyle\frac{m\pi(1+x_n)}2})}{(1-\frac{\tau_{l+1}-\tau_l}2\sin(\frac{\pi x_j}2)+\frac{\tau_{l+1}+\tau_l}2)^{\nu_1}} +\end{equation} +so +\begin{equation} + (\mathds 1_{n,l''}\odot\mathfrak a)_{m,l}= + \sum_{l,p}\sum_{l'}\frac{2-\delta_{l,0}}2w_n\frac{\cos({\textstyle\frac{l\pi(1+x_n)}2})}{(1-\frac{\tau_{l''+1}-\tau_{l''}}2\sin(\frac{\pi x_n}2)+\frac{\tau_{l''+1}+\tau_{l''}}2)^{\nu_1}}A_{l'',l;l',p}^{(\nu_1,\nu_a)}(k_{l,m})\mathfrak F_{l',p}^{(\nu_a)}(\mathfrak a) + . +\end{equation} +\bigskip + +\subsubpoint +We now turn to $a\hat\ast\hat v$. +Let +\begin{equation} + \Upsilon(t,|k|):= + \int_{||k|-t|}^{|k|+t}ds\ \frac{s\hat v(s)}{|k|} + \label{Upsilon} +\end{equation} +We have +\begin{equation} + a\hat\ast\hat v=\frac 1{4\pi^2}\int_0^\infty dt\ ta(t)\Upsilon(t,|k|) + . + \label{avUpsilon} +\end{equation} +Following the integration scheme\-~(\ref{anyeq_integration}), +\begin{equation} + (\mathfrak a\odot\mathfrak v)_{l,i}= + \frac1{32\pi}\sum_{l'=0}^{J-1}(\tau_{l'+1}-\tau_{l'})\sum_{j=1}^Nw_j \cos({\textstyle\frac{\pi x_j}2})(1+\vartheta_{l'}(x_j))^2\vartheta_{l'}(x_j)\mathfrak a_{l',j}\Upsilon(\vartheta(x_j),k_{l,i}) + . + \label{adotv} +\end{equation} +At $k=0$, +\begin{equation} + \left<\mathfrak a\mathfrak v\right>= + \frac1{32\pi}\sum_{l'=0}^{J-1}(\tau_{l'+1}-\tau_{l'})\sum_{j=1}^Nw_j \cos({\textstyle\frac{\pi x_j}2})(1+\vartheta_{l'}(x_j))^2\vartheta_{l'}(x_j)\mathfrak a_{l',j}\Upsilon(\vartheta(x_j),0) + \label{} +\end{equation} +with +\begin{equation} + \Upsilon(t,0)=2tv(t) + . +\end{equation} +The quantities $\Upsilon(\vartheta(x_n),k_{l,i})$ and $\Upsilon(\vartheta(x_n),0)$ are independent of $a$ and can be computed once and for all at execution. +The integral in\-~(\ref{Upsilon}) is computed using Gauss-Legendre quadratures, but without splitting into splines. +To maintain a high precision, we set the order of the integration to \refname{param:anyeq_J}{{\tt J}}$\times$\refname{param:anyeq_N}{{\tt N}}. +\bigskip + +\subsubpoint +Finally, +\begin{equation} + (\mathds 1_{l',n}\odot\mathfrak v)_{l,i}= + \frac{\tau_{l'+1}-\tau_{l'}}{32\pi}w_n\cos({\textstyle\frac{\pi x_n}2})(1+\vartheta_{l'}(x_n))^2\vartheta_{l'}(x_n)\Upsilon(\vartheta(x_n),k_{l,i}) +\end{equation} +and +\begin{equation} + \left<\mathds 1_{l',n}\mathfrak v\right>= + \frac{\tau_{l'+1}-\tau_{l'}}{32\pi}w_n\cos({\textstyle\frac{\pi x_n}2})(1+\vartheta_{l'}(x_n))^2\vartheta_{l'}(x_n)\Upsilon(\vartheta(x_n),0) + . +\end{equation} +\bigskip + +\point{\bf Evaluating $\hat l_3$.} +The only term in\-~(\ref{anyeq}) that does not involve just convolutions (whose computation was described above) is $\hat l_3$ (\ref{l3}). +To evaluate it, we first change variables, using a generalization of bipolar coordinates (see lemma\-~\ref{lemma:bipolar2}): +\begin{equation} + \begin{largearray} + \hat l_3(|k|) + = + \frac1{(2\pi)^5\rho^2| k|^2}\int_0^\infty dt\int_{|| k|-t|}^{| k|+t}ds\int_0^\infty dt'\int_{|| k|-t'|}^{| k|+t'}ds'\int_0^{2\pi}d\theta\ sts't'U(s)U(t)U(s')U(t') + \cdot\\\hfill\cdot + S(\mathfrak R(s,t,s',t',\theta,| k|)) + \end{largearray} +\end{equation} +with +\begin{equation} + \begin{largearray} + \mathfrak R(s,t,s',t',\theta,| k|) + := + \frac1{\sqrt 2| k|} + \Big( + | k|^2(s^2+t^2+(s')^2+(t')^2)-| k|^4-(s^2-t^2)((s')^2-(t')^2) + \\[0.5cm]\hfill + - + \sqrt{(4| k|^2s^2-(| k|^2+s^2-t^2)^2)(4| k|^2(s')^2-(| k|^2+(s')^2-(t')^2)^2)}\cos\theta + \Big)^{\frac12} + . + \end{largearray} +\end{equation} +We change variables as in\-~(\ref{convvar}): +\begin{equation} + \begin{largearray} + \hat l_3(|k|) + = + \frac1{(2\pi)^5\rho^2| k|^2} + \int_{-1}^1 d\tau\ \frac{2(1-\tau)}{(1+\tau)^3}U({\textstyle\frac{1-\tau}{1+\tau}}) + \int_{\alpha_-(| k|,\tau)}^{\alpha_+(| k|,\tau)} d\sigma\ \frac{2(1-\sigma)}{(1+\sigma)^3}U({\textstyle\frac{1-\sigma}{1+\sigma}}) + \int_{-1}^1 d\tau'\ \frac{2(1-\tau')}{(1+\tau')^3} + \cdot\\[0.5cm]\hfill\cdot + U({\textstyle\frac{1-\tau'}{1+\tau'}}) + \int_{\alpha_-(| k|,\tau')}^{\alpha_+(| k|,\tau')} d\sigma'\ \frac{2(1-\sigma')}{(1+\sigma')^3}U({\textstyle\frac{1-\sigma'}{1+\sigma'}}) + \int_0^{2\pi}d\theta\ S(\mathfrak R({\textstyle \frac{1-\sigma}{1+\sigma},\frac{1-\tau}{1+\tau},\frac{1-\sigma'}{1+\sigma'},\frac{1-\tau'}{1+\tau'},\theta,| k|})) + . + \end{largearray} +\end{equation} +We expand $U$ and $ S$ into Chebyshev polynomials as in\-~(\ref{a_chebyshev_spline}), and split the integrals into splines: +\begin{equation} + \hat l_3(|k_{l,j}|) + \approx + (\mathbb U^{\otimes4}\otimes\mathbb S)_{l,j}:= + \sum_{\lambda_1,\cdots,\lambda_5=0}^{J-1} + \sum_{n_1,\cdots,n_5=1}^\infty + \bar A_{\lambda_1,n_1;\cdots;\lambda_5,n_5}^{(\nu)}(|k_{l,j}|) + \prod_{i=1}^4 + \left(\mathfrak F_{\lambda_i,n_i}^{(\nu)}(\mathbb U)\right) + \mathfrak F_{\lambda_5,n_5}^{(\nu)}(\mathbb S) + \label{otimes} +\end{equation} +with $\mathbb U_{l,j}:=U(k_{l,j})$, $\mathbb S_{l,j}:=S(k_{l,j})$ and +\begin{equation} + \begin{largearray} + \bar A_{\lambda_1,n_1;\cdots;\lambda_5,n_5}^{(\nu)}(| k|) + = + \frac1{(2\pi)^5\rho^2| k|^2} + \int_{\tau_{\lambda_1}}^{\tau_{\lambda_1+1}} d\tau\ \frac{2(1-\tau)}{(1+\tau)^{3-\nu}}T_{n_1}({\textstyle\frac{2\tau-(\tau_{\lambda_1}+\tau_{\lambda_1+1})}{\tau_{\lambda_1+1}-\tau_{\lambda_1}}}) + \cdot\\[0.5cm]\hskip1cm\cdot + \mathds 1_{\alpha_-(| k|,\tau)<\tau_{\lambda_2+1}}\mathds 1_{\alpha_+(| k|,\tau)>\tau_{\lambda_2}} + \int_{\max(\tau_{\lambda_2},\alpha_-(| k|,\tau))}^{\min(\tau_{\lambda_2+1},\alpha_+(| k|,\tau))} d\sigma\ \frac{2(1-\sigma)}{(1+\sigma)^{3-\nu}}T_{n_2}({\textstyle\frac{2\sigma-(\tau_{\lambda_2}+\tau_{\lambda_2+1})}{\tau_{\lambda_2+1}-\tau_{\lambda_2}}}) + \cdot\\[0.5cm]\hskip1cm\cdot + \int_{\tau_{\lambda_3}}^{\tau_{\lambda_3+1}} d\tau'\ \frac{2(1-\tau')}{(1+\tau')^{3-\nu}}T_{n_3}({\textstyle\frac{2\tau'-(\tau_{\lambda_3}+\tau_{\lambda_3+1})}{\tau_{\lambda_3+1}-\tau_{\lambda_3}}}) + \cdot\\[0.5cm]\hskip1cm\cdot + \mathds 1_{\alpha_-(| k|,\tau')<\tau_{\lambda_4+1}}\mathds 1_{\alpha_+(| k|,\tau')>\tau_{\lambda_4}} + \int_{\max(\tau_{\lambda_4},\alpha_-(| k|,\tau'))}^{\min(\tau_{\lambda_4+1},\alpha_+(| k|,\tau'))} d\sigma'\ \frac{2(1-\sigma')}{(1+\sigma')^{3-\nu}}T_{n_4}({\textstyle\frac{2\sigma'-(\tau_{\lambda_4}+\tau_{\lambda_4+1})}{\tau_{\lambda_4+1}-\tau_{\lambda_4}}}) + \cdot\\[0.5cm]\hskip1cm\cdot + \int_0^{2\pi}d\theta\ + \frac{2^\nu}{(2+\mathfrak R)^\nu} + T_{n_5}({\textstyle\frac{2\frac{1-\mathfrak R}{1+\mathfrak R}-(\tau_{\lambda_5}+\tau_{\lambda_5+1})}{\tau_{\lambda_5+1}-\tau_{\lambda_5}}}) + \mathds 1_{\tau_{\lambda_5}<\frac{1-\mathfrak R}{1+\mathfrak R}<\tau_{\lambda_5+1}} + \end{largearray} +\end{equation} +in which $\mathfrak R$ is a shorthand for $\mathfrak R({\textstyle \frac{1-\sigma}{1+\sigma},\frac{1-\tau}{1+\tau},\frac{1-\sigma'}{1+\sigma'},\frac{1-\tau'}{1+\tau'},\theta,| k|})$. +Note that $\bar A$ is independent of $U$, and can be computed once and for all at the beginning of execution. +Since the tensor $\bar A$ is quite large (it contains $(NJ)^5$ entries), and its computation can be rather long it can be inconvenient to compute $\bar A$ at every execution. +Instead, one can use the \refname{command:anyeq_save_Abar}{{\tt save\_Abar}} method to compute $\bar A$ and save it to a file, which can then be recalled via the {\tt -s} option on the command line. +\bigskip + +\point{\bf Main algorithm to compute $U$.} +We are now ready to detail how $U\equiv\rho\hat u$ is computed. +All of the observables we are interested in are approximated from the values $\mathbb U_{l,j}:=U(k_{l,j})$ (\ref{klj}). +\bigskip + +\subpoint +The equation for $(\mathbb U_{l,j})_{l\in\{0,\cdots,J-1\},j\in\{1,\cdots,N\}}$ is obtained by approximating\-~(\ref{anyeq_hatu}) according to the prescriptions detailed above: +\begin{equation} + \mathbb U_{l,j}:= + \frac{1+\mathbb Y_{l,j}}{2(\mathbb X_{l,j}+1)}\ \Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + \label{bbU} +\end{equation} +\begin{equation} + \mathbb X_{l,j}:=\frac1{\mathbb L_{l,j}}\left(\mathbb K_{l,j}-\mathbb L_{l,j}+\frac{k_{l,j}^2}{2\rho}+\mathbb I_{l,j}\right) + ,\quad + \mathbb Y_{l,j}:=\frac1{\mathbb L_{l,j}}\left(\mathbb S_{l,j}-\mathbb L_{l,j}+\frac12\mathbb J_{l,j}+\mathbb G_{l,j}\right) +\end{equation} +\begin{equation} + \mathbb S_{l,j}:= + \mathfrak v_{l,j}-\frac1{\rho}(\mathbb U\odot\mathfrak v)_{l,j} + ,\quad + \mathfrak v_{l,j}:=\hat v(k_{l,j}) + ,\quad + \mathbb E:=\hat v(0)-\frac1{\rho}\left<\mathbb U\mathfrak v\right> +\end{equation} +\begin{equation} + \mathbb I_{l,j}:=\frac1{\rho}\gamma_{L,2}(\beta_{L,2}(\mathbb U\odot(\mathbb U\mathbb S))_{l,j}+(1-\beta_{L,2})\mathbb E(\mathbb U\odot\mathbb U)_{l,j}) +\end{equation} +\begin{equation} + \mathbb K_{l,j}:=\gamma_K(\beta_K\mathbb S_{l,j}+(1-\beta_K)\mathbb E) + ,\quad + \mathbb L_{l,j}:=\gamma_{L,1}(\beta_{L,1}\mathbb S_{l,j}+(1-\beta_{L,1})\mathbb E) +\end{equation} +\begin{equation} + \mathbb J_{l,j}:=\gamma_{L,3}(1-\beta_{L,3})\frac{\mathbb E}{\rho^2}(\mathbb U\odot\mathbb U)^2_{l,j} + +\gamma_{L,3}\beta_{L,3}(\mathbb U^{\otimes4}\otimes\mathbb S)_{l,j} +\end{equation} +\begin{equation} + \begin{largearray} + \mathbb G_{l,j}:= + \frac2{\rho}\gamma_K\alpha_K(\mathbb U\odot((\beta_K\mathbb S+(1-\beta_K)\mathbb E)\mathbb U))_{l,j} + \\[0.5cm]\hfill + - + \frac1{\rho}\alpha_{L,1}(\mathbb U\odot((\beta_{L,1}\mathbb S+(1-\beta_{L,1})\mathbb E)\mathbb U^2))_{l,j} + + + \frac2{\rho}\alpha_{L,2}(\mathbb U\odot(\mathbb I\mathbb U))_{l,j} + - + \frac1{2\rho}\alpha_{L,3}(\mathbb U\odot \mathbb J)_{l,j} + \end{largearray} +\end{equation} +(see\-~(\ref{odot}), (\ref{otimes}) and\-~(\ref{avg}) for the definitions of $\odot$, $\otimes$ and $\left<\cdot\right>$). +\bigskip + +\subpoint +We rewrite\-~(\ref{bbU}) as a root finding problem: +\begin{equation} + \Xi_{l,j}(\mathbb U):= + \mathbb U_{l,j}- + \frac{1+\mathbb Y_{l,j}}{2(\mathbb X_{l,j}+1)}\ \Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + =0 + \label{root_anyeq} +\end{equation} +which we solve using the Newton algorithm, that is, we define a sequence of $\mathbb U$'s: +\begin{equation} + \mathbb U^{(n+1)}=\mathbb U^{(n)}-(D\Xi(\mathbb U^{(n)}))^{-1}\Xi(\mathbb U^{(n)}) +\end{equation} +where $D\Xi$ is the Jacobian of $\Xi$: +\begin{equation} + (D\Xi)_{l,j;l',i}:=\frac{\partial\Xi_{l,j}}{\partial\mathbb U_{l',i}} + . +\end{equation} +\bigskip + +\subpoint +We initialize the algorithm with the solution of the \refname{eq:easyeq_medeq}{Medium equation}, which is computed using the \refname{sec:easyeq}{{\tt easyeq}} method. +However, \refname{sec:easyeq}{{\tt easyeq}} only computes $\hat u$ at the momenta given by the Gauss-Legendre quadratures\-~(\ref{easyeq_ki}). +To obtain a value for $\hat u$ at $k_{l,j}$, we use a linear interpolation (code is provided for a polynomial interpolation, which has not performed as well). +The parameters \refname{param:anyeq_tolerance}{{\tt tolerance}}, \refname{param:anyeq_maxiter}{{\tt maxiter}}, \refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}}, \refname{param:anyeq_nlrho_init}{{\tt nlrho\_init}} are passed on to \refname{sec:easyeq}{easyeq} as is, and the order of the Gauss-Legendre quadratures is set to \refname{param:anyeq_J}{{\tt J}}$\times$\refname{param:anyeq_N}{{\tt N}}. +\bigskip + +\subpoint +We are left with computing the Jacobian of $\Xi$: +\begin{equation} + \begin{largearray} + \partial_{l',i}\Xi_{l,j}\equiv + \frac{\partial\Xi_{l,j}}{\partial\mathbb U_{l',i}}= + \delta_{l,l'}\delta_{i,j} + +\frac1{2(\mathbb X_{l,j}+1)}\left( + \frac{(1+\mathbb Y_{l,j})\partial_{l',i}\mathbb X_{l,j}}{\mathbb X_{l,j}+1} + - + \partial_{l',i}\mathbb Y_{l,j} + \right) + \Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + \\[0.5cm]\hfill + +\frac{(1+\mathbb Y_{l,j})}{2(\mathbb X_{l,j}+1)^3}\left( + \frac{2(1+\mathbb Y_{l,j})}{\mathbb X_{l,j}+1}\partial_{l',i}\mathbb X_{l,j} + -\partial_{l',i}\mathbb Y_{l,j} + \right)\ \partial\Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + \end{largearray} +\end{equation} +with +\begin{equation} + \partial_{l',i}\mathbb X_{l,j}= + \frac1{\mathbb L_{l,j}}\left(\partial_{l',i}\mathbb K_{l,j}-\partial_{l',i}\mathbb L_{l,j}+\partial_{l',i}\mathbb I_{l,j}\right) + - + \frac{\partial_{l',i}\mathbb L_{l,j}}{\mathbb L_{l,j}}\mathbb X_{l,j} +\end{equation} +\begin{equation} + \partial_{l',i}\mathbb Y_{l,j}= + \frac1{\mathbb L_{l,j}}\left(\partial_{l',i}\mathbb S_{l,j}-\partial_{l',i}\mathbb L_{l,j}+\frac12\partial_{l',i}\mathbb J_{l,j}+\partial_{l',i}\mathbb G_{l,j}\right) + - + \frac{\partial_{l',i}\mathbb L_{l,j}}{\mathbb L_{l,j}}\mathbb Y_{l,j} +\end{equation} +\begin{equation} + \partial_{l',i}\mathbb S_{l,j}= + -\frac1{\rho}(\mathds 1_{l',i}\odot\mathfrak v)_{l,j} + ,\quad + \partial_{l',i}\mathbb E=-\frac1{\rho}\left<\mathds 1_{l',i}\mathfrak v\right> +\end{equation} +\begin{equation} + \partial_{l',i}\mathbb K_{l,j}:=\gamma_K(\beta_K\partial_{l',i}\mathbb S_{l,j}+(1-\beta_K)\partial_{l',i}\mathbb E) + ,\quad + \partial_{l',i}\mathbb L_{l,j}:=\gamma_{L,1}(\beta_{L,1}\partial_{l',i}\mathbb S_{l,j}+(1-\beta_{L,1})\partial_{l',i}\mathbb E) +\end{equation} +\begin{equation} + \begin{largearray} + \partial_{l',i}\mathbb I_{l,j}= + \frac1{\rho}\gamma_{L,2}\left(\beta_{L,2}( + \mathds 1_{l',i}\odot(\mathbb U\mathbb S) + + + \mathbb U\odot(\mathds 1_{l',i}\mathbb S+\mathbb U\partial_{l',i}\mathbb S) + )_{l,j} + \right.\\[0.5cm]\hfill\left. + + + (1-\beta_{L,2})( + \partial_{l',i}\mathbb E(\mathbb U\odot\mathbb U)_{l,j} + + + 2\mathbb E(\mathbb U\odot\mathds 1_{l',i})_{l,j} + )\right) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \partial_{l',i}\mathbb J_{l,j}= + \frac1{\rho^2}\gamma_{L,3}(1-\beta_{L,3})\left( + \partial_{l',i}\mathbb E(\mathbb U\odot\mathbb U)^2_{l,j} + + + 4\mathbb E(\mathbb U\odot\mathbb U)_{l,j}(\mathbb U\odot\mathds 1_{l',i})_{l,j} + \right) + \\[0.5cm]\hfill + +\gamma_{L,3}\beta_{L,3}( + 4\mathds 1_{l',i}\otimes\mathbb U^{\otimes3}\otimes\mathbb S + + + \mathbb U^{\otimes4}\otimes\partial_{l',i}\mathbb S + )_{l,j} + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \partial_{l',i}\mathbb G_{l,j}= + \frac2{\rho}\gamma_K\alpha_K\beta_K\left( + \mathds 1_{l',i}\odot(\mathbb S\mathbb U) + + + \mathbb U\odot(\mathbb S\mathds 1_{l',i} + +\partial_{l',i}\mathbb S\mathbb U) + \right)_{l,j} + \\[0.5cm]\hfill + +\frac2{\rho}\gamma_K\alpha_K(1-\beta_K)\left( + \partial_{l',i}\mathbb E(\mathbb U\odot\mathbb U)_{l,j} + +2\mathbb E(\mathds 1_{l',i}\odot\mathbb U)_{l,j} + \right) + \\[0.5cm]\indent + - + \frac1{\rho}\alpha_{L,1}\beta_{L,1}\left( + \mathds 1_{l',i}\odot(\mathbb S\mathbb U^2) + + + \mathbb U\odot( + \partial_{l',i}\mathbb S\mathbb U^2 + + + 2\mathbb S\mathbb U\mathds 1_{l',i} + ) + \right)_{l,j} + \\[0.5cm]\hfill + - + \frac1{\rho}\alpha_{L,1}(1-\beta_{L,1})\left( + \mathbb E\mathds 1_{l',i}\odot\mathbb U^2 + + + \mathbb U\odot( + \partial_{l',i}\mathbb E\mathbb U^2 + + + 2\mathbb E\mathbb U\mathds 1_{l',i} + ) + \right)_{l,j} + \\[0.5cm]\hfill + + + \frac2{\rho}\alpha_{L,2}( + (\mathds 1_{l',i}\odot(\mathbb I\mathbb U))_{l,j} + + + \mathbb U\odot( + \partial_{l',i}\mathbb I\mathbb U + + + \mathbb I\mathds 1_{l',i}) + )_{l,j} + - + \frac1{2\rho}\alpha_{L,3}( + \mathds 1_{l',i}\odot \mathbb J + + + \mathbb U\odot\partial_{l',i}\mathbb J + )_{l,j} + . + \end{largearray} +\end{equation} +\bigskip + +\subpoint +We iterate the Newton algorithm until the Newton relative error $\epsilon$ becomes smaller than the \refname{param:easyeq_tolerance}{{\tt tolerance}} parameter. +The Newton error is defined as +\begin{equation} + \epsilon=\frac{\|\mathbb U^{(n+1)}-\mathbb U^{(n)}\|_2}{\|\mathbb U^{(n)}\|_2} +\end{equation} +where $\|\cdot\|_2$ is the $l_2$ norm. +The energy thus obtained is +\begin{equation} + e=\frac\rho2\mathds E +\end{equation} +the Fourier transform $\hat u$ of the solution is +\begin{equation} + \hat u(k_{l,j})\approx\frac{\mathbb U_{l,j}}\rho +\end{equation} +where $k_{l,j}$ was defined in\-~(\ref{klj}), and the solution $u$ in real space is obtained by inverting the Fourier transform, following the prescription of\-~(\ref{anyeq_integration}): +\begin{equation} + u(|x|)=\int \frac{dk}{(2\pi)^3}\ e^{-ikx}\hat u(|k|) + \approx + \sum_{l=0}^{J-1} + \frac{\tau_{l+1}-\tau_l}{16\pi} + \sum_{j=1}^Nw_j + \cos({\textstyle\frac{\pi x_j}2})(1+\vartheta_l(x_j))^2\vartheta_l(x_j)\mathbb U_{l,j}\sin(\vartheta_l(x_j)|x|) + . +\end{equation} +\bigskip + +\point{\bf Condensate fraction.} +To compute the condensate fraction, we solve the modified {\tt anyeq} (see\-~\cite{CJL20b}): +\begin{equation} + (-\Delta+2\mu)u_\mu + = + (1-u_\mu)v-2\rho K+\rho^2 L + . +\end{equation} +where $K$ and $L$ are defined as in\-~(\ref{anyeq_K})-(\ref{anyeq_e}) in which $u$ is replaced with $u_\mu$. +The uncondensed fraction is then +\begin{equation} + \eta=\partial_\mu e|_{\mu=0} + =-\frac\rho2\int dx\ v(x)\partial_\mu u_\mu(x)|_{\mu=0} + . +\end{equation} +To compute the energy in the presence of the parameter $\mu$, we proceed in the same way as for $\mu=0$, the only difference being that $k^2$ should formally be replaced by $k^2+2\mu$. +In other words, we consider $\mathbb U_{j,l}=u_\mu(|k_{j,l}|)$ and define $\Xi(\mathbb U,\mu)$ in the same way as in\-~(\ref{root_anyeq}), except that $\mathbb X_i$ should be replaced by +\begin{equation} + \mathbb X_{l,j}=\frac1{\mathbb L_{l,j}}\left(\mathbb K_{l,j}-\mathbb L_{l,j}+\frac{k_{l,j}^2+2\mu}{2\rho}+\mathbb I_{l,j}\right) + . +\end{equation} +We then solve +\begin{equation} + \Xi(\mathbb U_\mu,\mu)=0 +\end{equation} +By differentiating this identity with respect to $\mu$, we find $\partial_\mu u_\mu$: +\begin{equation} + \partial_\mu \mathbb U|_{\mu=0}=-(D\Xi)^{-1}\partial_\mu\Xi|_{\mu=0} +\end{equation} +and +\begin{equation} + \partial_\mu\Xi|_{\mu=0}= + \frac{(1+\mathbb Y_{l,j})\partial_\mu\mathbb X_{l,j}}{2(\mathbb X_{l,j}+1)^2} + \Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + +\frac{(1+\mathbb Y_{l,j})^2}{(\mathbb X_{l,j}+1)^4}\partial_\mu\mathbb X_{l,j} + \partial\Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + ,\quad + \partial_\mu\mathbb X_{l,j}=\frac1{\rho\mathbb L_{l,j}} + . +\end{equation} +We then approximate +\begin{equation} + \eta\approx + -\frac12\left<\mathfrak v\partial_\mu\mathbb U\right> +\end{equation} +(see\-~(\ref{avg})). +\bigskip + +\point{\bf Correlation function.} +The two-point correlation function is +\begin{equation} + C_2(x):= + 2\rho\frac{\delta e}{\delta v(x)} + . +\end{equation} +In Fourier space, +\begin{equation} + C_2(x)= + 2\rho\int dk\ e^{ikx}\frac{\delta e}{\delta\hat v(k)} + =2\rho\int dk\ \frac{\sin(|k||x|)}{|k||x|}\frac{\delta e}{\delta\hat v(k)} + . + \label{C2fourier} +\end{equation} +\bigskip + +\subpoint +We can compute this quantity by considering a modified {\tt anyeq} in Fourier space, by formally replacing $\hat v$ with +\begin{equation} + \hat v+\lambda g(|k|) + ,\quad + g(|k|):=\frac{\sin(|k||x|)}{|k||x|} + . +\end{equation} +Indeed, if $e_\lambda$ denotes the energy of this modified equation, +\begin{equation} + \partial_\lambda e_\lambda|_{\lambda=0} + =\int dk\ \frac{\delta e}{\hat v(k)}\partial_\lambda({\textstyle \hat v(k)+\lambda g(|k|)}) + =\int dk\ g(|k|)\frac{\delta e}{\hat v(k)} +\end{equation} +so, denoting the solution of the modified equation by $u_\lambda$, +\begin{equation} + C_2(x)=2\rho\partial_\lambda e_\lambda|_{\lambda=0} + =-\rho^2\int dx\ (g(x)u_0(x)+v(x)\partial_\lambda u_\lambda(x)|_{\lambda=0}) + . +\end{equation} +We compute $\partial_\lambda u_\lambda|_{\lambda=0}$ in the same way as the uncondensed fraction: we define $\Xi(\mathbb U,\lambda)$ by formally adding $\lambda g(|k|)$ to $\hat v$, solve $\Xi(\mathbb U,\lambda)=0$, and differentiate: +\begin{equation} + \partial_\lambda\mathbb U|_{\lambda=0}=-(D\Xi)^{-1}\partial_\lambda\Xi|_{\lambda=0} + . +\end{equation} +\bigskip + +\subpoint +We compute $\partial_\lambda\Xi|_{\lambda=0}$: +\begin{equation} + \begin{largearray} + \partial_\lambda\Xi_{l,j}= + \delta_{l,l'}\delta_{i,j} + +\frac1{2(\mathbb X_{l,j}+1)}\left( + \frac{(1+\mathbb Y_{l,j})\partial_\lambda\mathbb X_{l,j}}{\mathbb X_{l,j}+1} + - + \partial_\lambda\mathbb Y_{l,j} + \right) + \Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + \\[0.5cm]\hfill + +\frac{(1+\mathbb Y_{l,j})}{2(\mathbb X_{l,j}+1)^3}\left( + \frac{2(1+\mathbb Y_{l,j})}{\mathbb X_{l,j}+1}\partial_\lambda\mathbb X_{l,j} + -\partial_\lambda\mathbb Y_{l,j} + \right)\ \partial\Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + \end{largearray} +\end{equation} +with +\begin{equation} + \partial_\lambda\mathbb S_{l,j}= + \mathfrak g_{l,j} + -\frac1{\rho}(\mathbb U\odot\mathfrak g)_{l,j} + ,\quad + \partial_\lambda\mathbb E=g(0)-\frac1{\rho}\left<\mathbb U\mathfrak g\right> +\end{equation} +\begin{equation} + \partial_\lambda\mathbb X_{l,j}= + \frac1{\mathbb L_{l,j}}\left(\partial_\lambda\mathbb K_{l,j}-\partial_\lambda\mathbb L_{l,j}+\partial_\lambda\mathbb I_{l,j}\right) + - + \frac{\partial_\lambda\mathbb L_{l,j}}{\mathbb L_{l,j}}\mathbb X_{l,j} +\end{equation} +\begin{equation} + \partial_\lambda\mathbb Y_{l,j}= + \frac1{\mathbb L_{l,j}}\left(\partial_\lambda\mathbb S_{l,j}-\partial_\lambda\mathbb L_{l,j}+\frac12\partial_\lambda\mathbb J_{l,j}+\partial_\lambda\mathbb G_{l,j}\right) + - + \frac{\partial_\lambda\mathbb L_{l,j}}{\mathbb L_{l,j}}\mathbb Y_{l,j} +\end{equation} +\begin{equation} + \partial_\lambda\mathbb K_{l,j}:=\gamma_K(\beta_K\partial_\lambda\mathbb S_{l,j}+(1-\beta_K)\partial_\lambda\mathbb E) + ,\quad + \partial_\lambda\mathbb L_{l,j}:=\gamma_{L,1}(\beta_{L,1}\partial_\lambda\mathbb S_{l,j}+(1-\beta_{L,1})\partial_\lambda\mathbb E) +\end{equation} +\begin{equation} + \partial_\lambda\mathbb I_{l,j}= + \frac1{\rho}\gamma_{L,2}\left( + \beta_{L,2}(\mathbb U\odot(\mathbb U\partial_\lambda\mathbb S)_{l,j} + + + (1-\beta_{L,2})\partial_\lambda\mathbb E(\mathbb U\odot\mathbb U)_{l,j} + \right) +\end{equation} +\begin{equation} + \partial_\lambda\mathbb J_{l,j}= + \frac1{\rho^2}\gamma_{L,3}(1-\beta_{L,3})\partial_\lambda\mathbb E(\mathbb U\odot\mathbb U)^2_{l,j} + +\gamma_{L,3}\beta_{L,3}(\mathbb U^{\otimes4}\otimes\partial_\lambda\mathbb S)_{l,j} +\end{equation} +\begin{equation} + \begin{largearray} + \partial_\lambda\mathbb G_{l,j}= + \frac2{\rho}\gamma_K\alpha_K\beta_K\left(\partial_\lambda\mathbb S\mathbb U\right)_{l,j} + +\frac2{\rho}\gamma_K\alpha_K(1-\beta_K)\partial_\lambda\mathbb E(\mathbb U\odot\mathbb U)_{l,j} + - + \frac1{\rho}\alpha_{L,1}\beta_{L,1}\left(\mathbb U\odot\partial_\lambda\mathbb S\mathbb U^2\right)_{l,j} + \\[0.5cm]\hfill + - + \frac1{\rho}\alpha_{L,1}(1-\beta_{L,1})\partial_\lambda\mathbb E\left(\mathbb U\odot(\mathbb U^2)\right)_{l,j} + + + \frac2{\rho}\alpha_{L,2}\mathbb U\odot(\partial_\lambda\mathbb I\mathbb U)_{l,j} + - + \frac1{2\rho}\alpha_{L,3}(\mathbb U\odot\partial_\lambda\mathbb J)_{l,j} + . + \end{largearray} +\end{equation} +To evaluate $(\mathbb U\odot\mathfrak g)$ and $\left<\mathbb U\mathfrak g\right>$, we proceed as in\-~(\ref{adotv}) and\-~(\ref{}). +To do so, we replace $\hat v$ with $g$ in the computation of $\Upsilon$. +\bigskip + +\subpoint +In order to invert the Fourier transform in\-~(\ref{C2fourier}) numerically, we will use a Hann window (see appendix\-~\ref{appendix:hann}) +\begin{equation} + H_L(k):=\mathds 1_{|k|<\frac L2}\cos^2({\textstyle\frac{\pi|k|}{L}}) + . + \label{hann} +\end{equation} +The parameter $L$ is set using \refname{param:anyeq_window_L}{{\tt window\_L}}. +The computation is changed only in that $g$ is changed to $H_L(k)\frac{\sin(|k||x|)}{|k||x|}$. +\bigskip + +\point {\bf Momentum distribution.} +To compute the momentum distribution (see\-~\cite{CHe21}), we add a parameter $\lambda$ to {\tt anyeq}: +\begin{equation} + -\Delta u_\lambda(|x|) + = + (1-u_\lambda(|x|))v(|x|)-2\rho K(|x|)+\rho^2 L(|x|) + -2\lambda \hat u_0(q)\cos(q\cdot x) +\end{equation} +($\hat u_0\equiv\hat u_\lambda|_{\lambda=0}$). +The momentum distribution is then +\begin{equation} + \mathcal M(q)=\partial_\lambda e|_{\lambda=0} + =-\frac\rho2\int dx\ v(x)\partial_\lambda u_\lambda(x)|_{\lambda=0} + . +\end{equation} +Note that the Fourier transform of $2\lambda\hat u_0(q)\cos(q\cdot x)$ is +\begin{equation} + -(2\pi)^3\lambda\hat u_0(q)(\delta(q+k)+\delta(q-k)) + . + \label{fouriermomentum} +\end{equation} +We compute $\partial_\lambda u_\lambda|_{\lambda=0}$ in the same way as the uncondensed fraction. +\bigskip + +\subpoint +In order to do so we will discretize momentum space, see\-~(\ref{klj}), and so it is necessary to construct a discrete analog of the delta-functions in\-~(\ref{fouriermomentum}). +The starting point we take is +\begin{equation} + \int dk f(k)\delta(k-q) + =f(q) +\end{equation} +so, when approximating the integral according to\-~(\ref{anyeq_integration}), we find +\begin{equation} + \frac{\pi^2}2\sum_{l=0}^{J-1} + (\tau_{l+1}-\tau_l) + \sum_{j=1}^Nw_j + \cos({\textstyle\frac{\pi x_j}2})(1+\vartheta_l(x_j))^2\vartheta_l^2(x_j)f(\vartheta_l(x_j))\tilde\delta(\vartheta_l(x_j)-k_{l',i}) + \approx + f(k_{l',i}) +\end{equation} +where $\tilde\delta$ is the approximation of the delta-function. +Since +\begin{equation} + \vartheta_l(x_j)\equiv k_{l,j} +\end{equation} +(see\-~(\ref{chebyshevvars})), we find that the definition of $\tilde\delta$ must be +\begin{equation} + \tilde\delta_{l,j;l',i} + := + \delta_{l,l'}\delta_{j,i} + \frac2{\pi^2} + \left( + (\tau_{l+1}-\tau_l) + w_j + \cos({\textstyle\frac{\pi x_j}2}) + (1+k_{l,j})^2k_{l,j}^2 + \right)^{-1} + . +\end{equation} +Note that, due to the invariance under rotation, the approximation for $\delta(q+k)$ is equal to that for $\delta(q-k)$. +\bigskip + +\subpoint +To compute the momentum distribution at $q=k_{l',i}$, we define $\Xi(\mathbb U,\lambda)$ by formally adding $-2(2\pi)^3\lambda \hat u_0(k_{l',i})\tilde\delta_{l,j;l',i}$ to $\mathbb G_{l,j}$, which corresponds to replacing $\mathbb Y$ with +\begin{equation} + \mathbb Y_{l,j}= + \frac1{\mathbb L_{l,j}}\left(\mathbb S_{l,j}-\mathbb L_{l,j}+\frac12\mathbb J_{l,j}+\mathbb G_{l,j}-2(2\pi)^3\lambda\frac{\mathbb U_{l',i}}\rho\tilde\delta_{l,j;l',i}\right) +\end{equation} +Then we solve $\Xi(\mathbb U,\lambda)=0$, and differentiate: +\begin{equation} + \partial_\lambda\mathbb U|_{\lambda=0}=-(D\Xi)^{-1}\partial_\lambda\Xi|_{\lambda=0} + . +\end{equation} +Finally, +\begin{equation} + \partial_\lambda\Xi_{l,j}|_{\lambda=0} + = + -\partial_\lambda\mathbb Y_{l,j}|_{\lambda=0}\left( + \frac1{2(\mathbb X_{l,j}+1)} + \Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + +\frac{(1+\mathbb Y_{l,j})}{2(\mathbb X_{l,j}+1)^3} + \partial\Phi\left({\textstyle\frac{1+\mathbb Y_{l,j}}{(\mathbb X_{l,j}+1)^2}}\right) + \right) +\end{equation} +with +\begin{equation} + \partial_\lambda\mathbb Y_{l,j}|_{\lambda=0}= + \frac{2(2\pi)^3\mathbb U_{l',i}\tilde\delta_{l,j;l',i}}{\rho\mathbb L_{l,j}} + . +\end{equation} + +\subsection{{\tt simpleq-Kv}}\label{sec:simpleq-Kv} +\indent +The method is used to compute observables for the simple equation +\begin{equation} + -\Delta u + = + v(1-u)-4eu+2e\rho u\ast u + ,\quad + e=\frac\rho2\int dx\ (1-u(|x|))v(|x|) + . + \label{simpleq} +\end{equation} +One can show\-~\cite[Theorem\-~1.6]{CJL20b} that the condensate fraction is +\begin{equation} + \eta=\frac{\rho\int v(x)\mathfrak Ku(x)\ dx}{1-\rho\int v(x)\mathfrak K(2u(x)-\rho u\ast u(x))\ dx} + \label{simpleq-Kv_eta} +\end{equation} +with +\begin{equation}\label{bg6} + \mathfrak K = (-\Delta + v + 4e(1 - \rho u\ast))^{-1} + . + \label{Kv} +\end{equation} +Similarly, the two-point correlation function is\-~\cite[(45)]{CHe21} +\begin{equation} + C_2= + \rho^2(1-u)+ + \rho^2\frac{\mathfrak Kv(1-u)-2\rho u\ast \mathfrak Kv+\rho^2u\ast u\ast \mathfrak Kv}{1-\rho\int dx\ v(x)\mathfrak K(2u(x)-\rho u\ast u(x))} + . + \label{simpleq-Kv_C2} +\end{equation} +Thus, using the fact that $\mathfrak K$ is self-adjoint, we can compute these observables of the simple equation directly from the knowledge of $\mathfrak Kv$. +\bigskip + +\subsubsection{Usage} +\indent +The computation uses the same approximation scheme as \refname{sec:anyeq}{{\tt anyeq}}, as well as using the solution of \refname{sec:anyeq}{{\tt anyeq}}. +As such, it takes a similar list of parameters: +\refname{param:anyeq_rho}{{\tt rho}}, +\refname{param:anyeq_tolerance}{{\tt tolerance}}, +\refname{param:anyeq_maxiter}{{\tt maxiter}}, +\refname{param:anyeq_P}{{\tt P}}, +\refname{param:anyeq_N}{{\tt N}}, +\refname{param:anyeq_J}{{\tt J}}, +\refname{param:anyeq_minlrho_init}{{\tt minlrho\_init}}, +\refname{param:anyeq_nlrho_init}{{\tt nlrho\_init}}. +\bigskip + +The available {\tt commands} are the following. +\bigskip + +\begin{itemize} + \item\makelink{command:simpleq-Kv_Kv}{} + {\tt Kv}: compute $\mathfrak Kv$ as a function of $|x|$.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:simpleq-Kv_xmin}{} + {\tt xmin} ({\tt Float64}, default: 0): minimum of the range of $|x|$ to be printed. + + \item\makelink{param:simpleq-Kv_xmax}{} + {\tt xmax} ({\tt Float64}, default: 100): maximum of the range of $|x|$ to be printed. + + \item\makelink{param:simpleq-Kv_nx}{} + {\tt nx} ({\tt Int64}, default: 100): number of points to print (linearly spaced). + + \end{itemize} + \underline{Output} (one line for each value of $|x|$): [$|x|$] [$\mathfrak K v$] + + \item\makelink{command:simpleq-Kv_condensate-fraction}{} + {\tt condensate-fraction}: compute the uncondensed fraction as a function of $\rho$\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:simpleq-Kv_minlrho}{} + {\tt minlrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\log_{10}\rho$. + + \item\makelink{param:simpleq-Kv_maxlrho}{} + {\tt maxlrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\log_{10}\rho$. + + \item\makelink{param:simpleq-Kv_nlrho}{} + {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically). + + \item\makelink{param:simpleq-Kv_rhos}{} + {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list. + \end{itemize} + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$\eta$] + + \item\makelink{command:simpleq-Kv_2pt}{} + {\tt 2pt}: compute the two-point correlation as a function of $|x|$.\par + \underline{Extra parameters}: same as \refname{command:simpleq-Kv_Kv}{{\tt Kv}}.\par + \underline{Output} (one line for each value of $|x|$): [$|x|$] [$C_2$] +\end{itemize} + +\subsubsection{Description} +\indent +In Fourier space\-~(\ref{anyeq_fourier}), +\begin{equation} + \hat{\mathfrak K}^{-1}f + := + \int dx\ e^{ikx} + (-\Delta+4e(1-\rho u\ast)+v)f + =(k^2+4e(1-\rho\hat u(|k|))+\hat v\hat\ast)\hat f +\end{equation} +where $\hat\ast$ is defined in\-~(\ref{anyeq_hatast}). +We follow the same approximation scheme as \refname{sec:anyeq}{{\tt anyeq}}: +\begin{equation} + \hat{\mathfrak K}^{-1}f(k_{l,j}) + \approx(k_{l,j}^2+4e(1-\mathbb U_{l,j}))\mathfrak f_{l,j} + + + (\mathfrak v\odot\mathfrak f)_{l,j} +\end{equation} +with $\mathfrak f_{l,j}:=f(k_{l,j})$, $\mathbb U_{l,j}:=\rho u(|k_{l,j}|)$, $\odot$ is defined in\-~(\ref{odot}), and $k_{l,j}$ is defined in\-~(\ref{klj}). +Therefore, we approximate the operator $\hat{\mathfrak K}^{-1}$ by a matrix: +\begin{equation} + \hat{\mathfrak K}^{-1}f(k_{l,j})\approx + \sum_{l'=0}^{J-1}\sum_{i=1}^N M_{l,j;l',i}\mathfrak f_{l',i} +\end{equation} +with, by\-~(\ref{odot}) and\-~(\ref{frakF}), +\begin{equation} + \begin{largearray} + M_{l,j;l',i}:= + \delta_{l',l}\delta_{j,i}(k_{l,j}^2+4e(1-\mathbb U_{l,j})) + \\\hfill + + + \frac1{4\pi^2|k_{l,j}|}\sum_{n,m=0}^P\sum_{l''=0}^{J-1}\mathfrak F_{l'',n}^{(\nu_v)}(\mathfrak v) + A_{l'',n;l',m}^{(\nu_v,\nu_f)}(|k_{l,j}|) + \frac{\frac{2-\delta_{m,0}}2w_i\cos(\frac{m\pi(1+x_i)}2)}{(1-\frac{\tau_{l'+1}-\tau_{l'}}2\sin(\frac{\pi x_i}2)+\frac{\tau_{l'+1}+\tau_{l'}}2)^{\nu_f}} + . + \end{largearray} +\end{equation} +Defining $\mathds 1_{l',i}$ as the vector whose only non-vanishing component is that indexed by $l',i$ which is equal to 1, we can rewrite +\begin{equation} + M_{l,j;l',i}:= + \delta_{l',l}\delta_{j,i}(k_{l,j}^2+4e(1-\mathbb U_{l,j})) + + + (\mathfrak v\odot\mathds 1_{l',i})_{l,j} + . +\end{equation} +Thus +\begin{equation} + \mathfrak Kv\approx M^{-1}\mathfrak v + . +\end{equation} +\bigskip + +\indent +To compute\-~(\ref{simpleq-Kv_eta}), we write +\begin{equation} + \eta=\frac{\rho\int\frac{dk}{(2\pi)^3} \hat u(k)\hat{\mathfrak K}\hat v(k)}{1-\rho\int \frac{dk}{(2\pi)^3)}(2\hat u(k)-\rho\hat u^2(k))\hat{\mathfrak K}\hat v(k)} +\end{equation} +which we approximate as +\begin{equation} + \eta\approx\frac{\left<\mathbb UM^{-1}\mathfrak v\right>}{1-\left<(2\mathbb U-\mathbb U^2)M^{-1}\mathfrak v\right>} +\end{equation} +where $\left<\cdot\right>$ is defined in\-~(\ref{avg}). +We can thus compute $\eta$ using the solution $\mathbb U$ computed by \refname{sec:anyeq}{{\tt anyeq}}. +\bigskip + +\indent +To compute\-~(\ref{simpleq-Kv_C2}), we write +\begin{equation} + C_2= + \rho^2(1-u(x))+ + \rho^2\frac{\mathfrak Kv(x)(1-u(x))+\int\frac{dk}{(2\pi)^3}(-2\rho\hat u\hat{\mathfrak K}\hat v+\rho^2\hat u^2\hat{\mathfrak K}\hat v)}{1-\rho\int\frac{dk}{(2\pi)^3}\ (2\hat u(k)-\rho \hat u^2(k))\hat{\mathfrak K}\hat v(k)} +\end{equation} +which we approximate as +\begin{equation} + C_2\approx + \rho^2-\rho\left+ + \rho^2\frac{\left(1-\rho^{-1}\left)+\left<(-2\mathbb U M^{-1}\mathfrak v+\mathbb UM^{-1}\mathfrak v)\right>}{1-\left<(2\mathbb U-\mathbb U^2)M^{-1}\mathfrak v\right>} + . +\end{equation} +We can thus compute $C_2$ using the solution $\mathbb U$ computed by \refname{sec:anyeq}{{\tt anyeq}}. + +\subsection{\tt simpleq-hardcore}\label{sec:simpleq-hardcore} +\indent +This method is used to solve the Simple equation with a hardcore potential: +\begin{equation} + \left\{\begin{array}{>\displaystyle ll} + (-\Delta+4e) u(x)=2e\rho u\ast u(x) + &\mathrm{for\ }|x|>1\\ + u(x)=1 + &\mathrm{for\ }|x|\leqslant1 + \end{array}\right. + \label{linearhc} +\end{equation} +with +\begin{equation} + e=-\frac{4\pi\rho\partial u|_{|x|\searrow 1}}{2(1-\frac83\pi\rho+\rho^2\int_{|x|<1}dx\ u\ast u(x))} + . + \label{hardcore_energy} +\end{equation} +\bigskip + +This equation is solved in $x$-space, and as such is very different from \refname{sec:easyeq}{{\tt easyeq}}, and significantly longer to run. +\bigskip + +\subsubsection{Usage} +\indent +Unless otherwise noted, this method takes the following parameters (specified via the {\tt [-p params]} flag, as a `{\tt;}' separated list of entries). +\begin{itemize} + \item\makelink{param:simpleq-hardcore_rho}{} + {\tt rho} ({\tt Float64}, default: $10^{-6}$): density $\rho$. + + \item\makelink{param:simpleq-hardcore_tolerance}{} + {\tt tolerance} ({\tt Float64}, default: $10^{-11}$): maximal size of final step in Newton iteration. + + \item\makelink{param:simpleq-hardcore_maxiter}{} + {\tt maxiter} ({\tt Int64}, default: 21): maximal number of iterations of the Newton algorithm before giving up. + + \item\makelink{param:simpleq-hardcore_P}{} + {\tt P} ({\tt Int64}, default: 11): order of all Chebyshev polynomial expansions (denoted by $P$ below). + + \item\makelink{param:simpleq-hardcore_N}{} + {\tt N} ({\tt Int64}, default: 12): order of all Gauss quadratures (denoted by $N$ below). + + \item\makelink{param:simpleq-hardcore_J}{} + {\tt J} ({\tt Int64}, default: 10): number of splines (denoted by $J$ below). +\end{itemize} +\bigskip + +The available {\tt commands} are the following. + +\begin{itemize} + \item\makelink{command:simpleq-hardcore_energy_rho}{} + {\tt energy\_rho}: compute the energy $e$ as a function of $\rho$.\par + \underline{Disabled parameters}: \refname{param:simpleq-hardcore_rho}{{\tt rho}}.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:simpleq-hardcore_minlrho}{} + {\tt minlrho} ({\tt Float64}, default: $10^{-6}$): minimal value for $\log_{10}\rho$. + + \item\makelink{param:simpleq-hardcore_maxlrho}{} + {\tt maxlrho} ({\tt Float64}, default: $10^{2}$): maximal value for $\log_{10}\rho$. + + \item\makelink{param:simpleq-hardcore_nlrho}{} + {\tt nlrho} ({\tt Int64}, default: $100$): number of values for $\rho$ (spaced logarithmically). + + \item\makelink{param:simpleq-hardcore_rhos}{} + {\tt rhos} ({\tt Array\{Float64\}}, default: $(10^{{\tt minlrho}+\frac{{\tt maxlrho}-{\tt minlrho}}{{\tt nlrho}}n})_n$: list of values for $\rho$, specified as a `{\tt,}' separated list. + \end{itemize} + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$e$] [Newton error $\epsilon$].\par + \underline{Multithread support}: yes, different values of $\rho$ split up among workers. + + \item\makelink{command:simpleq-hardcore_condensate_fraction_rho}{} + {\tt condensate\_fraction\_rho}: compute the uncondensed fraction $\eta$ as a function of $\rho$.\par + \underline{Disabled parameters}: same as \refname{command:simpleq-hardcore_energy_rho}{{\tt energy\_rho}}.\par + \underline{Extra parameters}: same as \refname{command:simpleq-hardcore_energy_rho}{{\tt energy\_rho}}.\par + \underline{Output} (one line for each value of $\rho$): [$\rho$] [$\eta$] [Newton error $\epsilon$].\par + \underline{Multithread support}: yes, different values of $\rho$ split up among workers. + + \item\makelink{command:simpleq-hardcore_ux}{} + {\tt ux}: compute $u$ as a function of $|x|$.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:simpleq-hardcore_xmin}{} + {\tt xmin} ({\tt Float64}, default: 0): minimum of the range of $|x|$ to be printed. + + \item\makelink{param:simpleq-hardcore_xmax}{} + {\tt xmax} ({\tt Float64}, default: 100): maximum of the range of $|x|$ to be printed. + + \item\makelink{param:simpleq-hardcore_nx}{} + {\tt nx} ({\tt Int64}, default: 100): number of points to print (linearly spaced). + + \end{itemize} + \underline{Output} (one line for each value of $x$): [$|x|$] [$u(|x|)$] + +\end{itemize} + +\subsubsection{Description} +\indent +In order to carry out the computation of the solution of\-~(\ref{linearhc}) and compute the condensate fraction at the same time, we will consider the equation with an added parameter $\mu>0$: +\begin{equation} + (-\Delta+4\epsilon)u=2e\rho u\ast u + ,\quad + \epsilon:=e+\frac\mu2 + \label{hardcore_simple} +\end{equation} +for $|x|>1$. +\bigskip + +\point{\bf Energy.} +To compute the energy $e$ of this equation, with the extra parameter $\mu$, we consider the limit of the soft sphere potential $\lambda\mathds 1_{|x|<1}$ (see\-~(\ref{easyeq}) with $\beta_K=\beta_L=0$): +\begin{equation} + (-\Delta+2\mu+4e)u(x)=s_\lambda(x)+2e\rho u\ast u + ,\quad + s_\lambda(x):=\lambda(1-u(x))\mathds 1_{|x|\leqslant 1} + ,\quad + \frac{2e}\rho=\int dx\ s_\lambda(x) + . +\end{equation} +Furthermore, since $\partial u$ need not be continuous at $|x|=1$, by integrating $-\Delta u$ over a thin spherical shell of radius 1, we find that, for $|x|\leqslant 1$, +\begin{equation} + -\Delta u(x)=-\delta(|x|-1)\partial u|_{|x|\searrow1} +\end{equation} +so, formally, +\begin{equation} + s_\infty(x)=\mathds 1_{|x|\leqslant 1}(2e(2-\rho u\ast u(x))+2\mu)-\delta(|x|-1)\partial u|_{|x|\searrow1} +\end{equation} +and +\begin{equation} + \frac{2e}\rho=\int dx\ s_\infty(x) + = + 2e\left(\frac{8\pi}3-\rho\int_{|x|<1}u\ast u(x)\right)+\frac{8\pi}3\mu-4\pi\partial u|_{|x|\searrow1} + . +\end{equation} +Therefore, +\begin{equation} + e=\frac{2\pi\rho(\frac23\mu-\partial u|_{|x|\searrow1})}{1-\frac{8\pi}3\rho+\rho^2\int_{|x|<1}dx\ u\ast u(x)} + . + \label{emu} +\end{equation} +\bigskip + +\point{\bf Integral equation.} +We turn the differential equation in\-~(\ref{hardcore_simple}) into an integral equation. +Let +\begin{equation} + w(|x|):=|x|u(x) +\end{equation} +we have, for $r>1$, +\begin{equation} + (-\partial_r^2+4\epsilon)w(r)=2e\rho ru\ast u(r) + . +\end{equation} +Furthermore, the bounded solution of +\begin{equation} + (-\partial_r^2+\alpha^2)w(r)=F(r) + ,\quad + w(1)=1 +\end{equation} +is +\begin{equation} + w(r)=e^{-\alpha(r-1)}+\int_0^\infty ds\ \frac{F(s+1)}{2\alpha}\left(e^{-\alpha|(r-1)-s|}-e^{-\alpha((r-1)+s)}\right) + \label{solw} +\end{equation} +so, for $r>1$, +\begin{equation} + u(r)= + \frac 1re^{-2\sqrt\epsilon(r-1)} + + + \frac{\rho e}{2r\sqrt\epsilon}\int_0^\infty ds\ (s+1)(u\ast u(s+1))\left(e^{-2\sqrt\epsilon|(r-1)-s|}-e^{-2\sqrt\epsilon((r-1)+s)}\right) + . +\end{equation} +In order to compute the integral more easily, we split it: +\begin{equation} + \begin{largearray} + u(r)= + \frac 1re^{-2\sqrt\epsilon(r-1)} + + + \frac{\rho e}{r\sqrt\epsilon}\int_0^{r-1} ds\ (s+1)(u\ast u(s+1))\sinh(2\sqrt\epsilon s)e^{-2\sqrt\epsilon(r-1)} + \\[0.5cm]\hfill + + + \frac{\rho e}{r\sqrt\epsilon}\sinh(2\sqrt\epsilon(r-1))\int_{r-1}^\infty ds\ (s+1)(u\ast u(s+1))e^{-2\sqrt\epsilon s} + . + \end{largearray} +\end{equation} +We change variables in the last integral: +\begin{equation} + \begin{largearray} + u(r)= + \frac 1re^{-2\sqrt\epsilon(r-1)} + + + \frac{\rho e}{r\sqrt\epsilon}\int_0^{r-1} ds\ (s+1)(u\ast u(s+1))\sinh(2\sqrt\epsilon s)e^{-2\sqrt\epsilon(r-1)} + \\[0.5cm]\hfill + + + \frac{\rho e}{2r\sqrt\epsilon}\left(1-e^{-4\sqrt\epsilon(r-1)}\right)\int_0^\infty d\sigma\ (\sigma+r)(u\ast u(\sigma+r))e^{-2\sqrt\epsilon\sigma} + . + \end{largearray} + \label{uinteq} +\end{equation} +\bigskip + +\point{\bf The auto-convolution term.} We split +\begin{equation} + u(r)=\mathds 1_{r>1}u_+(r-1)+\mathds 1_{r\leqslant 1} + . +\end{equation} +in terms of which +\begin{equation} + u\ast u= + \mathds 1_{r\leqslant 1}\ast\mathds 1_{r\leqslant 1} + +2\mathds 1_{r\leqslant 1}\ast(u_+(r-1)\mathds 1_{r>1}) + +(\mathds 1_{r>1}u_+(r-1))\ast(u_+(r-1)\mathds 1_{r>1}) + \label{u*u_dcmp} +\end{equation} +In bipolar coordinates (see lemma\-~\ref{lemma:bipolar}), +\begin{equation} + \mathds 1_{r\leqslant 1}\ast\mathds 1_{r\leqslant 1}(r) + = + \frac{2\pi}{r}\int_0^1 dt\ t\int_{|r-t|}^{r+t}ds\ s\mathds 1_{s\leqslant 1} + \label{1*1} +\end{equation} +and, if $r\geqslant 0$ and $t\geqslant 0$, then +\begin{equation} + \int_{|r-t|}^{r+t}ds\ s\mathds 1_{s\leqslant 1} + = + \mathds 1_{r-1\leqslant t\leqslant r+1}\int_{|r-t|}^{\min(1,r+t)}ds\ s + = + \mathds 1_{r-1\leqslant t\leqslant r+1} + \left( + \mathds 1_{t\leqslant 1-r} + 2rt + + + \mathds 1_{t>1-r} + \frac{1-(r-t)^2}2 + \right) + . + \label{int1} +\end{equation} +Therefore, if $r\geqslant 1$ +\begin{equation} + \mathds 1_{r\leqslant 1}\ast\mathds 1_{r\leqslant 1}(r) + = + \mathds 1_{r\leqslant 2} + \frac{2\pi}{r}\int_{r-1}^1 dt\ t + \frac{1-(r-t)^2}2 + = + \mathds 1_{r\leqslant 2}\frac\pi{12}(r-2)^2(r+4) + \label{u*u_1} +\end{equation} +and if $r<1$, +\begin{equation} + \mathds 1_{r\leqslant 1}\ast\mathds 1_{r\leqslant 1}(r) + = + \frac{2\pi}r\int_0^1dt\ t + \left( + \mathds 1_{t\leqslant 1-r} + 2rt + + + \mathds 1_{t>1-r} + \frac{1-(r-t)^2}2 + \right) +\end{equation} +so +\begin{equation} + \mathds 1_{r\leqslant 1}\ast\mathds 1_{r\leqslant 1}(r) + =\frac43\pi(1-r)^3 + +\frac\pi{12}r(36-48r+17r^2) + = + \frac\pi{12}(r-2)^2(r+4) + . +\end{equation} +Thus, (\ref{u*u_1}) holds for all $r$. +Furthermore, +\begin{equation} + 2\mathds 1_{r\leqslant 1}\ast(u_+(r-1)\mathds 1_{r>1}) + = + \frac{4\pi}{r}\int_1^\infty dt\ tu_+(t-1)\int_{|r-t|}^{r+t}ds\ s\mathds 1_{s\leqslant 1} +\end{equation} +so, if $r\geqslant 0$ then, by\-~(\ref{int1}), +\begin{equation} + 2\mathds 1_{r\leqslant 1}\ast(u_+(r-1)\mathds 1_{r>1})(r) + = + \frac{2\pi}{r}\int_{\max(1,r-1)}^{r+1} dt\ tu_+(t-1)(1-(r-t)^2) + . + \label{u*u_cross} +\end{equation} +Finally, if $r\geqslant 0$ then +\begin{equation} + (\mathds 1_{r>1}u_+(r-1))\ast(u_+(r-1)\mathds 1_{r>1})(r) + = + \frac{2\pi}{r}\int_1^\infty dt\ tu_+(t-1)\int_{\max{(1,|r-t|)}}^{r+t}ds\ su_+(s-1) + . + \label{u*u_u} +\end{equation} +Thus, by~\-(\ref{u*u_1}), (\ref{u*u_cross}) and\-~(\ref{u*u_u}), for $r\geqslant -1$, +\begin{equation} + \begin{largearray} + u\ast u(1+r)= + \mathds 1_{r\leqslant 1}\frac\pi{12}(r-1)^2(r+5) + + + \frac{2\pi}{r+1}\int_{\max(0,r-1)}^{r+1} dt\ (t+1)u_+(t)(1-(r-t)^2) + \\[0.5cm]\hfill + + + \frac{2\pi}{r+1}\int_0^\infty dt\ (t+1)u_+(t)\int_{\max{(0,|r-t|-1)}}^{r+t+1}ds\ (s+1)u_+(s) + . + \end{largearray} +\end{equation} +We then compactify the integrals by changing variables to $\tau=\frac{1-t}{1+t}$ and $\sigma=\frac{1-s}{1+s}$: +\begin{equation} + \begin{largearray} + u\ast u(1+r)= + \mathds 1_{r\leqslant 1}\frac\pi{12}(r-1)^2(r+5) + + + \frac{8\pi}{r+1}\int_{-\frac r{2+r}}^{\min(1,\frac{2-r}r)} d\tau\ \frac1{(1+\tau)^3}u_+({\textstyle\frac{1-\tau}{1+\tau}})(1-(r-{\textstyle\frac{1-\tau}{1+\tau}})^2) + \\[0.5cm]\hfill + + + \frac{32\pi}{r+1}\int_{-1}^1 d\tau\ \frac1{(1+\tau)^3}u_+({\textstyle\frac{1-\tau}{1+\tau}})\int_{\alpha_-(1+r,\tau)}^{\min(1,\beta_+(r,\tau))}d\sigma\ \frac1{(1+\sigma)^3}u_+({\textstyle\frac{1-\sigma}{1+\sigma}}) + . + \end{largearray} + \label{u*u} +\end{equation} +with +\begin{equation} + \alpha_-(1+r,\tau):=\frac{-r-\frac{1-\tau}{1+\tau}}{2+r+\frac{1-\tau}{1+\tau}} + ,\quad + \beta_+(r,\tau):=\frac{2-|r-\frac{1-\tau}{1+\tau}|}{|r-\frac{1-\tau}{1+\tau}|} +\end{equation} +(note that $\alpha_-$ is the same as defined for fulleq). +Finally, note that, if $\beta_+<1$, that is, if $|r-\frac{1-\tau}{1+\tau}|>1$, then +\begin{equation} + \beta_+(r,\tau)=\alpha_+({\textstyle |r-\frac{1-\tau}{1+\tau}|-1+\frac{1-\tau}{1+\tau}},\tau) +\end{equation} +where +\begin{equation} + \alpha_+(r,\tau):=\frac{1-|r-\frac{1-\tau}{1+\tau}|}{1+|r-\frac{1-\tau}{1+\tau}|} +\end{equation} +is the same as is defined for \refname{sec:anyeq}{{\tt anyeq}} (\ref{anyeq_alpha}). +\bigskip + +\point{\bf Chebyshev polynomial expansion.} We use the same interpolation as we used in \refname{sec:anyeq}{{\tt anyeq}}: (\ref{a_chebyshev_spline}) +\begin{equation} + \frac1{(1+\tau)^{\nu_u}}u_+({\textstyle\frac{1-\tau}{1+\tau}})=\sum_{l=0}^{J-1}\mathds 1_{\tau_l\leqslant\tau<\tau_{l+1}}\sum_{n=0}^\infty \mathbf F_{l,n}^{(\nu_u)}(u_+) T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}}) + \label{a_chebyshev_spline} +\end{equation} +($J$ is set by the parameter \refname{param:simpleq-hardcore_J}{{\tt J}}) +with +\begin{equation} + \mathbf F_{l,n}^{(\nu_u)}(u_+):= + \frac{2-\delta_{n,0}}{\pi} + \int_0^{\pi} d\theta\ + \frac{\cos(n\theta)}{(1+\frac{\tau_{l+1}-\tau_l}2\cos(\theta)+\frac{\tau_{l+1}+\tau_l}2)^{\nu_u}} + u_+({\textstyle\frac{2-(\tau_{l+1}-\tau_l)\cos(\theta)-(\tau_{l+1}+\tau_\alpha)}{2+(\tau_{l+1}-\tau_l)\cos(\theta)+(\tau_{l+1}+\tau_l)}}) + \label{bfF} +\end{equation} +and we take $\nu_u$ to be the decay exponent of $u$, which we will assume is $\nu_u=4$. +In particular, by\-~(\ref{u*u}), +\begin{equation} + u\ast u(1+r)=\mathds 1_{r\leqslant 1}B^{(0)}(r)+\sum_l\sum_n\mathbf F_{l,n}^{(\nu_u)}(u_+)B^{(1)}_{l,n}(r)+\sum_{l,l'}\sum_{n,m}\mathbf F_{l,n}^{(\nu_u)}(u_+)\mathbf F_{l',m}^{(\nu_u)}(u_+)B^{(2)}_{l,n;l',m}(r) + \label{u*u} +\end{equation} +with +\begin{equation} + B^{(0)}(r):= + \frac\pi{12}(r-1)^2(r+5) +\end{equation} +\begin{equation} + B^{(1)}_{l,n}(r):= + \mathds 1_{\tau_l<\frac{2-r}r}\mathds 1_{\tau_{l+1}>-\frac r{2+r}} + \frac{8\pi}{r+1}\int_{\max(\tau_l,-\frac r{2+r})}^{\min(\tau_{l+1},\frac{2-r}r)} d\tau\ \frac1{(1+\tau)^{3-\nu_u}}(1-(r-{\textstyle\frac{1-\tau}{1+\tau}})^2)T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}}) +\end{equation} +\begin{equation} + \begin{largearray} + B^{(2)}_{l,n;l',m}(r):= + \frac{32\pi}{r+1}\int_{\tau_l}^{\tau_{l+1}} d\tau\ \frac1{(1+\tau)^{3-\nu_u}}T_n({\textstyle\frac{2\tau-(\tau_l+\tau_{l+1})}{\tau_{l+1}-\tau_l}}) + \mathds 1_{\tau_{l'}<\alpha_+(|r-\frac{1-\tau}{1+\tau}|-\frac{2\tau}{1+\tau},\tau)}\mathds 1_{\tau_{l'+1}>\alpha_-(1+r,\tau)} + \cdot\\[0.5cm]\hfill\cdot + \int_{\max(\tau_{l'},\alpha_-(1+r,\tau))}^{\min(\tau_{l'+1},\alpha_+(|r-\frac{1-\tau}{1+\tau}|-\frac{2\tau}{1+\tau},\tau))}d\sigma\ \frac1{(1+\sigma)^{3-\nu_u}}T_m({\textstyle\frac{2\sigma-(\tau_{l'}+\tau_{l'+1})}{\tau_{l'+1}-\tau_{l'}}}) + . + \end{largearray} +\end{equation} +Thus, by\-~(\ref{uinteq}), for $r>0$, +\begin{equation} + u_+(r,e,\epsilon)= + D^{(0)}(r,e,\epsilon) + + + \sum_l\sum_n\mathbf F_{l,n}^{(\nu_u)}(u_+)D^{(1)}_{l,n}(r,e,\epsilon) + + + \sum_{l,l'}\sum_{n,m}\mathbf F_{l,n}^{(\nu_u)}(u_+)\mathbf F_{l',m}^{(\nu_u)}(u_+)D^{(2)}_{l,n;l',m}(r,e,\epsilon) + \label{hardcore_u+} +\end{equation} +with +\begin{equation} + \begin{largearray} + D^{(0)}(r,e,\epsilon):= + \frac 1{r+1}e^{-2\sqrt\epsilon r} + + + \frac{\rho e}{(r+1)\sqrt\epsilon}\int_0^{\min(1,r)} ds\ (s+1)B^{(0)}(s)\sinh(2\sqrt\epsilon s)e^{-2\sqrt\epsilon r} + \\[0.5cm]\hfill + + + \mathds 1_{r\leqslant 1} + \frac{\rho e}{2(r+1)\sqrt\epsilon}\left(1-e^{-4\sqrt\epsilon r}\right)\int_0^{1-r} d\sigma\ (\sigma+r+1)B^{(0)}(\sigma+r)e^{-2\sqrt\epsilon\sigma} + \end{largearray} + \label{D0} +\end{equation} +\begin{equation} + \begin{largearray} + D^{(1)}_{l,n}(r,e,\epsilon):= + \frac{\rho e}{(r+1)\sqrt\epsilon}\int_0^r ds\ (s+1)B^{(1)}_{l,n}(s)\sinh(2\sqrt\epsilon s)e^{-2\sqrt\epsilon r} + \\[0.5cm]\hfill + + + \frac{\rho e}{2(r+1)\sqrt\epsilon}\left(1-e^{-4\sqrt\epsilon r}\right)\int_0^\infty d\sigma\ (\sigma+r+1)B_{l,n}^{(1)}(\sigma+r)e^{-2\sqrt\epsilon\sigma} + . + \end{largearray} + \label{D1} +\end{equation} +and +\begin{equation} + \begin{largearray} + D^{(2)}_{l,n;l',m}(r,e,\epsilon):= + \frac{\rho e}{(r+1)\sqrt\epsilon}\int_0^r ds\ (s+1)B^{(2)}_{l,n;l',m}(s)\sinh(2\sqrt\epsilon s)e^{-2\sqrt\epsilon r} + \\[0.5cm]\hfill + + + \frac{\rho e}{2(r+1)\sqrt\epsilon}\left(1-e^{-4\sqrt\epsilon r}\right)\int_0^\infty d\sigma\ (\sigma+r+1)B_{l,n;l',m}^{(2)}(\sigma+r)e^{-2\sqrt\epsilon\sigma} + . + \end{largearray} + \label{D2} +\end{equation} +\bigskip + +\point{\bf Energy.} We now compute the approximation for the energy, using\-~(\ref{hardcore_energy}). +\bigskip + +\subpoint We start with $\partial u|_{|x|\searrow 1}$. +By\-~(\ref{uinteq}), +\begin{equation} + \partial u|_{|x|\searrow 1} + = + -(1+2\sqrt\epsilon) + + + 2\rho e\int_0^\infty d\sigma\ (\sigma+1)(u\ast u(\sigma+1))e^{-2\sqrt\epsilon\sigma} +\end{equation} +\bigskip +which, by\-~(\ref{u*u}), becomes +\begin{equation} + \begin{largearray} + \partial u|_{|x|\searrow 1} + = + -(1+2\sqrt\epsilon) + + + \gamma^{(0)}(e,\epsilon) + + + \sum_l\sum_n\mathbf F_{l,n}^{(\nu_u)}(u_+)\gamma^{(1)}_{l,n}(e,\epsilon) + +\\\hfill+ + \sum_{l,l'}\sum_{n,m}\mathbf F_{l,n}^{(\nu_u)}(u_+)\mathbf F_{l',m}^{(\nu_u)}(u_+)\gamma^{(2)}_{l,n;l',m}(e,\epsilon) + \end{largearray} +\end{equation} +with +\begin{equation} + \gamma^{(0)}(e,\epsilon):= + 2\rho e\int_0^1 d\sigma\ (\sigma+1)B^{(0)}(\sigma)e^{-2\sqrt\epsilon\sigma} +\end{equation} +\begin{equation} + \gamma^{(1)}_{l,n}(e,\epsilon):= + 2\rho e\int_0^\infty d\sigma\ (\sigma+1)B_{l,n}^{(1)}(\sigma)e^{-2\sqrt\epsilon\sigma} +\end{equation} +and +\begin{equation} + \gamma^{(2)}_{l,n;l',m}(e,\epsilon):= + 2\rho e\int_0^\infty d\sigma\ (\sigma+1)B_{l,n;l',m}^{(2)}(\sigma)e^{-2\sqrt\epsilon\sigma} + . +\end{equation} + +\subpoint Let us now turn to $\int_{|x|<1}dx\ u\ast u(x)$. +We have +\begin{equation} + \int_{|x|<1}dx\ u\ast u(x) + = + 4\pi\int_0^1 dr\ r^2 u\ast u(r) +\end{equation} +so, by\-~(\ref{u*u}), +\begin{equation} + \int_{|x|<1}dx\ u\ast u(x) + = + \bar\gamma^{(0)}(r) + + + \sum_l\sum_n\mathbf F_{l,n}^{(\nu_u)}(u_+)\bar\gamma^{(1)}_{l,n}(r) + + + \sum_{l,l'}\sum_{n,m}\mathbf F_{l,n}^{(\nu_u)}(u_+)\mathbf F_{l',m}^{(\nu_u)}(u_+)\bar\gamma^{(2)}_{l,n;l',m}(r) + \label{intusu} +\end{equation} +with +\begin{equation} + \bar\gamma^{(0)}:= + 4\pi\int_0^1 d\sigma\ \sigma^2B^{(0)}(\sigma-1) +\end{equation} +\begin{equation} + \bar\gamma^{(1)}_{l,n}:= + 4\pi\int_0^1 d\sigma\ \sigma^2B_{l,n}^{(1)}(\sigma-1) +\end{equation} +and +\begin{equation} + \bar\gamma^{(2)}_{l,n;l',m}:= + 4\pi\int_0^1 d\sigma\ \sigma^2B_{l,n;l',m}^{(2)}(\sigma-1) + . +\end{equation} +\bigskip + +\subpoint Thus, +\begin{equation} + \begin{largearray} + e=2\pi\rho + \cdot\\\hfill\cdot + \frac + { + C(\epsilon) + - + \gamma^{(0)}(e,\epsilon) + - + \sum_l\sum_n\mathbf F_{l,n}^{(\nu_u)}(u_+)\gamma^{(1)}_{l,n}(e,\epsilon) + - + \sum_{l,l'}\sum_{n,m}\mathbf F_{l,n}^{(\nu_u)}(u_+)\mathbf F_{l',m}^{(\nu_u)}(u_+)\gamma^{(2)}_{l,n;l',m}(e,\epsilon) + } + {1-\frac83\pi\rho+\rho^2\left( + \bar\gamma^{(0)} + + + \sum_l\sum_n\mathbf F_{l,n}^{(\nu_u)}(u_+)\bar\gamma^{(1)}_{l,n} + + + \sum_{l,l'}\sum_{n,m}\mathbf F_{l,n}^{(\nu_u)}(u_+)\mathbf F_{l',m}^{(\nu_u)}(u_+)\bar\gamma^{(2)}_{l,n;l',m} + \right)} + . + \end{largearray} +\end{equation} +\begin{equation} + C(\epsilon):=\frac23\mu+(1+2\sqrt\epsilon) + =\frac43(\epsilon-e)+1+2\sqrt\epsilon +\end{equation} +\bigskip + +\point{\bf Newton algorithm.} +In this paragraph, we set $\epsilon=e$, that is, $\mu=0$. +\bigskip + +\subpoint +As we did for \refname{sec:anyeq}{{\tt anyeq}}, we discretize the integral in\-~(\ref{bfF}) by using a Gauss-Legendre quadrature, and truncate the sum over Chebyshev polynomials to order \refname{param:simpleq-hardcore_P}{{\tt P}}. +We then reduce the computation to a finite system of equations, whose variables are +\begin{equation} + e + ,\quad + \mathfrak u_{l,j}:=u_+(r_{l,j}) + ,\quad + r_{l,j}:= + \frac{2+(\tau_{l+1}-\tau_l)\sin(\frac{\pi x_j}2)-(\tau_{l+1}+\tau_\alpha)}{2-(\tau_{l+1}-\tau_l)\sin(\frac{\pi x_j}2)+(\tau_{l+1}+\tau_l)} +\end{equation} +where $x_j$ are the abcissa for Gauss-Legendre quadratures (see\-~(\ref{klj})). +In other words, we define a vector $\mathbb U$ in dimension \refname{param:simpleq-hardcore_N}{{\tt N}}$\times$\refname{param:simpleq-hardcore_J}{{\tt J}}$+1$, where the first \refname{param:simpleq-hardcore_N}{{\tt N}}$\times$\refname{param:simpleq-hardcore_J}{{\tt J}} terms are $\mathfrak u_{l,j}$ and the last component is $e$. +We then write\-~(\ref{hardcore_u+}) as, for $l\in\{0,\cdots,J-1\}$, $j\in\{1,\cdots,N\}$, +\begin{equation} + \Xi_{lN+j}(\mathbb U)=0 + ,\quad + \Xi_{NJ+1}(\mathbb U)=0 +\end{equation} +(note that $\Xi_{lN+j}$ corresponds to the pair $(l,j)$) +with +\begin{equation} + \begin{largearray} + \Xi_{lN+j}(\mathbb U):= + -\mathfrak u_{l,j} + + + \mathbb D^{(0)}(r_{l,j},e) + + + \sum_{l'}\sum_n\mathfrak F_{l',n}^{(\nu_u)}(\mathfrak u)\mathbb D^{(1)}_{l',n}(r_{l,j},e) + +\\\hfill+ + \sum_{l',l''}\sum_{n,m}\mathfrak F_{l',n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l'',m}^{(\nu_u)}(\mathfrak u)\mathbb D^{(2)}_{l',n;l'',m}(r_{l,j},e) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \Xi_{NJ+1}(\mathbb U):=-e + +\\\hfill+ + 2\pi\rho + \frac + { + C(e) + - + \mathfrak g^{(0)}(e) + - + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak g^{(1)}_{l,n}(e) + - + \sum_{l,l'}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l',m}^{(\nu_u)}(\mathfrak u)\mathfrak g^{(2)}_{l,n;l',m}(e) + } + {1-\frac83\pi\rho+\rho^2\left( + \bar{\mathfrak g}^{(0)} + + + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(1)}_{l,n} + + + \sum_{l,l'}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l',m}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(2)}_{l,n;l',m} + \right)} + . + \end{largearray} +\end{equation} +where $\mathfrak F$ is defined in\-~(\ref{frakF}), and $\mathbb D^{(i)}$, $\mathfrak g^{(i)}$, $\bar{\mathfrak g}^{(i)}$ are defined like $D^{(i)}$, $\gamma^{(i)}$ and $\bar\gamma^{(i)}$ except that the integrals over bounded intervals are approximated using Gauss-Legendre quadratures, and the integrals from $0$ to $\infty$ are approximated using Gauss-Laguerre quadratures. +Gauss-Legendre and Gauss-Laguerre quadratures and their errors are discussed in appendix\-~\ref{appGL}. +The orders of the quadratures are given by the variable \refname{param:simpleq-hardcore_N}{{\tt N}}. +\bigskip + +\subpoint +We then solve $\Xi=0$ using the Newton algorithm, that is, we define a sequence of $\mathbb U$'s: +\begin{equation} + \mathbb U^{(n+1)}=\mathbb U^{(n)}-(D\Xi(\mathbb U^{(n)}))^{-1}\Xi(\mathbb U^{(n)}) +\end{equation} +where $D\Xi$ is the Jacobian of $\Xi$: +\begin{equation} + (D\Xi)_{\alpha,\beta}:=\frac{\partial\Xi_\alpha}{\partial\mathbb U_\beta} + . +\end{equation} +We initialize the algorithm with +\begin{equation} + \mathbb U^{(0)}_{lN+j}=\frac1{(1+r_{l,j}^2)^2} + ,\quad + \mathbb U^{(0)}_{JN+1}=\pi\rho + . +\end{equation} +\bigskip + +\subpoint +We are left with computing the Jacobian of $\Xi$: +\begin{equation} + \begin{largearray} + \frac{\partial\Xi_{lN+j}(\mathbb U)}{\partial\mathfrak u_{l',i}}= + -\delta_{l,l'}\delta_{j,i} + + + \sum_{l''}\sum_n\mathfrak F_{l'',n}^{(\nu_u)}(\mathds 1_{l',i})\mathbb D^{(1)}_{l'',n}(r_{l,j},e) + +\\\hfill+ + 2\sum_{l'',l'''}\sum_{n,m}\mathfrak F_{l'',n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l''',m}^{(\nu_u)}(\mathds 1_{l',j})\mathbb D^{(2)}_{l'',n;l''',m}(r_{l,j},e) + \end{largearray} +\end{equation} +where $\mathds 1_{l',i}$ is a vector whose only non-vanishing entry is the $(l',i)$-th, which is 1, +\begin{equation} + \begin{largearray} + \frac{\partial\Xi_{JN+1}(\mathbb U)}{\partial\mathfrak u_{l',i}} + =\\\hfill= + 2\pi\rho + \frac + { + - + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathds 1_{l',i})\mathfrak g^{(1)}_{l,n}(e) + - + 2\sum_{l,l''}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l'',m}^{(\nu_u)}(\mathds 1_{l',i})\mathfrak g^{(2)}_{l,n;l'',m}(e) + } + {1-\frac83\pi\rho+\rho^2\left( + \bar{\mathfrak g}^{(0)} + + + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(1)}_{l,n} + + + \sum_{l,l''}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l'',m}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(2)}_{l,n;l'',m} + \right)} + \\[1cm]\hfill + -(\Xi_{NJ+1}(\mathbb U)+e) + \frac + {\rho^2\left( + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathds 1_{l'.i})\bar{\mathfrak g}^{(1)}_{l,n} + + + 2\sum_{l,l''}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l'',m}^{(\nu_u)}(\mathds 1_{l',i})\bar{\mathfrak g}^{(2)}_{l,n;l'',m} + \right)}{1-\frac83\pi\rho+\rho^2\left( + \bar{\mathfrak g}^{(0)} + + + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(1)}_{l,n} + + + \sum_{l,l''}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l'',m}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(2)}_{l,n;l'',m} + \right)} + . + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \frac{\partial\Xi_{lN+j}(\mathbb U)}{\partial e}= + \partial_e\mathbb D^{(0)}(r_{l,j},e) + + + \sum_{l'}\sum_n\mathfrak F_{l',n}^{(\nu_u)}(\mathfrak u)\partial_e\mathbb D^{(1)}_{l',n}(r_{l,j},e) + +\\\hfill+ + \sum_{l',l''}\sum_{n,m}\mathfrak F_{l',n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l'',m}^{(\nu_u)}(\mathfrak u)\partial_e\mathbb D^{(2)}_{l',n;l'',m}(r_{l,j},e) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \frac{\partial\Xi_{NJ+1}(\mathbb U)}{\partial e} + = + -1 + +\\\hfill+ + 2\pi\rho + \frac + { + \partial_e C(e) + - + \partial_e\mathfrak g^{(0)}(e) + - + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\partial_e\mathfrak g^{(1)}_{l,n}(e) + - + \sum_{l,l'}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l',m}^{(\nu_u)}(\mathfrak u)\partial_e\mathfrak g^{(2)}_{l,n;l',m}(e) + } + {1-\frac83\pi\rho+\rho^2\left( + \bar{\mathfrak g}^{(0)} + + + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(1)}_{l,n} + + + \sum_{l,l'}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l',m}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(2)}_{l,n;l',m} + \right)} + . + \end{largearray} +\end{equation} +To compute $\partial_e\mathbb D^{(i)}$ and $\partial_e\mathfrak g^{(i)}$, we use $\partial_e=\frac1{2\sqrt e}\frac\partial{\partial\sqrt e}$ and +\begin{equation} + \frac{\partial C(e)}{\partial\sqrt e}=2 +\end{equation} +\begin{equation} + \begin{largearray} + \frac{\partial D^{(0)}(r,e)}{\partial\sqrt e} + = + -\frac{2r}{r+1}e^{-2\sqrt er} + \\[0.5cm]\hfill + + + \frac{\rho}{r+1}\int_0^{\min(1,r)} ds\ (s+1)B^{(0)}(s)e^{-2\sqrt er}\left( + (1-2\sqrt er)\sinh(2\sqrt e s) + +2\sqrt es\cosh(2\sqrt e s) + \right) + \\[0.5cm] + + + \mathds 1_{r\leqslant 1} + \frac{\rho}{2(r+1)}\int_0^{1-r} d\sigma\ (\sigma+r+1)B^{(0)}(\sigma+r) + \cdot\\[0.5cm]\hfill\cdot + \left( + (1-2\sqrt e\sigma)\left(1-e^{-4\sqrt er}\right)e^{-2\sqrt e\sigma} + + + 4\sqrt e re^{-2\sqrt e(2r+\sigma)} + \right) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \frac{\partial D_{l,n}^{(1)}(r,e)}{\partial\sqrt e} + = + \frac{\rho}{r+1}\int_0^{r} ds\ (s+1)B_{l,n}^{(1)}(s)e^{-2\sqrt er}\left( + (1-2\sqrt er)\sinh(2\sqrt e s) + +2\sqrt es\cosh(2\sqrt e s) + \right) + \\[0.5cm] + + + \frac{\rho}{2(r+1)}\int_0^\infty d\sigma\ (\sigma+r+1)B_{l,n}^{(1)}(\sigma+r) + \cdot\\[0.5cm]\hfill\cdot + \left( + (1-2\sqrt e\sigma)\left(1-e^{-4\sqrt er}\right)e^{-2\sqrt e\sigma} + + + 4\sqrt e re^{-2\sqrt e(2r+\sigma)} + \right) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \frac{\partial D_{l,n;l',m}^{(2)}(r,e)}{\partial\sqrt e} + \\[0.5cm] + = + \frac{\rho}{r+1}\int_0^{r} ds\ (s+1)B_{l,n;l',m}^{(2)}(s)e^{-2\sqrt er}\left( + (1-2\sqrt er)\sinh(2\sqrt e s) + +2\sqrt es\cosh(2\sqrt e s) + \right) + \\[0.5cm] + + + \frac{\rho}{2(r+1)}\int_0^\infty d\sigma\ (\sigma+r+1)B_{l,n;l',m}^{(2)}(\sigma+r) + \cdot\\[0.5cm]\hfill\cdot + \left( + (1-2\sqrt e\sigma)\left(1-e^{-4\sqrt er}\right)e^{-2\sqrt e\sigma} + + + 4\sqrt e re^{-2\sqrt e(2r+\sigma)} + \right) + . + \end{largearray} +\end{equation} +Furthermore, +\begin{equation} + \frac{\partial\gamma^{(0)}(e)}{\partial\sqrt e}= + 4\rho\sqrt e\int_0^1 d\sigma\ (\sigma+1)B^{(0)}(\sigma)(1-\sqrt e\sigma)e^{-2\sqrt e\sigma} +\end{equation} +\begin{equation} + \frac{\partial\gamma^{(1)}_{l,n}(e)}{\partial\sqrt e}= + 4\rho e\int_0^\infty d\sigma\ (\sigma+1)B_{l,n}^{(1)}(\sigma)(1-\sqrt e\sigma)e^{-2\sqrt e\sigma} +\end{equation} +and +\begin{equation} + \frac{\partial\gamma^{(2)}_{l,n;l',m}(e)}{\partial\sqrt e}= + 4\rho e\int_0^\infty d\sigma\ (\sigma+1)B_{l,n;l',m}^{(2)}(\sigma)(1-\sqrt e\sigma)e^{-2\sqrt e\sigma} + . +\end{equation} +Finally, to get from $D$ to $\mathbb D$ and $\gamma$ to $\mathfrak g$, we approximate the integrate using Gauss-Legendre and Gauss-Laguerre quadratures (see appendix\-~\ref{appGL}), as described above. +\bigskip + +\subpoint +We iterate the Newton algorithm until the Newton relative error $\epsilon$ becomes smaller than the \refname{param:easyeq_tolerance}{{\tt tolerance}} parameter. +The Newton error is defined as +\begin{equation} + \epsilon=\frac{\|\mathbb U^{(n+1)}-\mathbb U^{(n)}\|_2}{\|\mathbb U^{(n)}\|_2} +\end{equation} +where $\|\cdot\|_2$ is the $l_2$ norm. +The energy thus obtained is +\begin{equation} + e=\mathbb U_{JN+1} + . +\end{equation} +\bigskip + +\point{\bf Condensate fraction.} +To compute the condensate fraction, we use the parameter $\mu$ in\-~(\ref{hardcore_simple}). +The uncondensed fraction is +\begin{equation} + \eta=\partial_\mu e|_{\mu=0} + . +\end{equation} +To compute $\partial_\mu e$, we use +\begin{equation} + \Xi(\mathbb U)=0 +\end{equation} +which we differentiate with respect to $\mu$: +\begin{equation} + \partial_\mu\mathbb U=-(D\Xi)^{-1}\partial_\mu\Xi + . +\end{equation} +We are left with computing $\partial_\mu\Xi$: +\begin{equation} + \begin{largearray} + \frac{\partial\Xi_{lN+j}(\mathbb U)}{\partial\mu}= + \partial_\mu\mathbb D^{(0)}(r_{l,j},e,\epsilon) + + + \sum_{l'}\sum_n\mathfrak F_{l',n}^{(\nu_u)}(\mathfrak u)\partial_\mu\mathbb D^{(1)}_{l',n}(r_{l,j},e,\epsilon) + +\\\hfill+ + \sum_{l',l''}\sum_{n,m}\mathfrak F_{l',n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l'',m}^{(\nu_u)}(\mathfrak u)\partial_\mu\mathbb D^{(2)}_{l',n;l'',m}(r_{l,j},e,\epsilon) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \frac{\partial\Xi_{NJ+1}(\mathbb U)}{\partial \mu} + =\\\hfill= + 2\pi\rho + \frac + { + \partial_\mu C(\epsilon) + - + \partial_\mu\mathfrak g^{(0)}(e,\epsilon) + - + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\partial_\mu\mathfrak g^{(1)}_{l,n}(e,\epsilon) + - + \sum_{l,l'}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l',m}^{(\nu_u)}(\mathfrak u)\partial_\mu\mathfrak g^{(2)}_{l,n;l',m}(e,\epsilon) + } + {1-\frac83\pi\rho+\rho^2\left( + \bar{\mathfrak g}^{(0)} + + + \sum_l\sum_n\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(1)}_{l,n} + + + \sum_{l,l'}\sum_{n,m}\mathfrak F_{l,n}^{(\nu_u)}(\mathfrak u)\mathfrak F_{l',m}^{(\nu_u)}(\mathfrak u)\bar{\mathfrak g}^{(2)}_{l,n;l',m} + \right)} + . + \end{largearray} +\end{equation} +We then use $\partial_\mu=\frac1{4\sqrt\epsilon}\partial_{\sqrt\epsilon}$ and +\begin{equation} + \left.\frac{\partial C(\epsilon)}{\partial\sqrt\epsilon}\right|_{\mu=0} + = + \frac83\sqrt\epsilon+2 +\end{equation} +\begin{equation} + \begin{largearray} + \left.\frac{\partial D^{(0)}(r)}{\partial\sqrt\epsilon}\right|_{\mu=0}= + -\frac{2r}{r+1}e^{-2\sqrt{e}r} + \\[0.5cm]\hfill + + + \frac{\rho}{(r+1)}\int_0^{\min(1,r)} ds\ (s+1)B^{(0)}(s)e^{-2\sqrt{e}r}((-1-2\sqrt er)\sinh(2\sqrt{e} s)+2\sqrt es\cosh(2\sqrt{e} s)) + \\[0.5cm] + + + \mathds 1_{r\leqslant 1} + \frac{\rho}{2(r+1)}\int_0^{1-r} d\sigma\ (\sigma+r+1)B^{(0)}(\sigma+r) + \cdot\\[0.5cm]\hfill\cdot + \left((-1-2\sqrt e\sigma)\left(1-e^{-4\sqrt er}\right)e^{-2\sqrt{e}\sigma}+4\sqrt ere^{-2\sqrt{e}(2r+\sigma)}\right) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \left.\frac{\partial D_{l,n}^{(1)}(r)}{\partial\sqrt\epsilon}\right|_{\mu=0} + = + \frac{\rho}{r+1}\int_0^{r} ds\ (s+1)B_{l,n}^{(1)}(s)e^{-2\sqrt er}\left( + (-1-2\sqrt er)\sinh(2\sqrt e s) + +2\sqrt es\cosh(2\sqrt e s) + \right) + \\[0.5cm] + + + \frac{\rho}{2(r+1)}\int_0^\infty d\sigma\ (\sigma+r+1)B_{l,n}^{(1)}(\sigma+r) + \cdot\\[0.5cm]\hfill\cdot + \left( + (-1-2\sqrt e\sigma)\left(1-e^{-4\sqrt er}\right)e^{-2\sqrt e\sigma} + + + 4\sqrt e re^{-2\sqrt e(2r+\sigma)} + \right) + \end{largearray} +\end{equation} +\begin{equation} + \begin{largearray} + \left.\frac{\partial D_{l,n;l',m}^{(2)}(r)}{\partial\sqrt\epsilon}\right|_{\mu=0} + \\[0.5cm] + = + \frac{\rho}{r+1}\int_0^{r} ds\ (s+1)B_{l,n;l',m}^{(2)}(s)e^{-2\sqrt er}\left( + (-1-2\sqrt er)\sinh(2\sqrt e s) + +2\sqrt es\cosh(2\sqrt e s) + \right) + \\[0.5cm] + + + \frac{\rho}{2(r+1)}\int_0^\infty d\sigma\ (\sigma+r+1)B_{l,n;l',m}^{(2)}(\sigma+r) + \cdot\\[0.5cm]\hfill\cdot + \left( + (-1-2\sqrt e\sigma)\left(1-e^{-4\sqrt er}\right)e^{-2\sqrt e\sigma} + + + 4\sqrt e re^{-2\sqrt e(2r+\sigma)} + \right) + . + \end{largearray} +\end{equation} +\begin{equation} + \left.\frac{\partial \gamma^{(0)}}{\partial\sqrt\epsilon}\right|_{\mu=0} + = + -4\rho e\int_0^1 d\sigma\ \sigma(\sigma+1)B^{(0)}(\sigma)e^{-2\sqrt e\sigma} +\end{equation} +\begin{equation} + \left.\frac{\partial\gamma^{(1)}_{l,n}}{\partial\sqrt\epsilon}\right|_{\mu=0} + = + -4\rho e\int_0^\infty d\sigma\ (\sigma+1)\sigma B_{l,n}^{(1)}(\sigma)e^{-2\sqrt e\sigma} +\end{equation} +and +\begin{equation} + \left.\frac{\partial\gamma^{(2)}_{l,n;l',m}}{\partial\sqrt\epsilon}\right|_{\mu=0} + = + -4\rho e\int_0^\infty d\sigma\ (\sigma+1)\sigma B_{l,n;l',m}^{(2)}(\sigma)e^{-2\sqrt e\sigma} + . +\end{equation} + +\subsection{\tt simpleq-iteration}\label{sec:simpleq-iteration} +\indent +This method is used to solve the Simple equation using the iteration described in\-~\cite{CJL20}. +The Simple equation is +\begin{equation} + -\Delta u + = + S-4e u+2e\rho u\ast u + \label{simpleq_iteration} +\end{equation} +\begin{equation} + S:=(1-u)v + ,\quad + \rho:=\frac{2e}{\int dx\ (1-u(|x|))v(|x|)} + . + \label{simpleq_Se} +\end{equation} +for a soft potential $v$ at fixed energy $e>0$. +The iteration is defined as +\begin{equation} + -\Delta u_n=S_n-4eu_n+2e\rho_{n-1}u_{n-1}\ast u_{n-1} + ,\quad + u_0=0 + ,\quad + \rho_{n-1}= + \frac{2e}{\int dx\ (1-u_{n-1}(|x|))v(|x|)} + . + \label{iteration} +\end{equation} +\bigskip + +\subsubsection{Usage} +\indent +Unless otherwise noted, this method takes the following parameters (specified via the {\tt [-p params]} flag, as a `{\tt;}' separated list of entries). +\begin{itemize} + \item\makelink{param:simpleq-iteration_e}{} + {\tt e} ({\tt Float64}, default: $10^{-4}$): energy $e$. + + \item\makelink{param:simpleq-iteration_maxiter}{} + {\tt maxiter} ({\tt Int64}, default: 21): maximal number of iterations. + + \item\makelink{param:simpleq-iteration_order}{} + {\tt order} ({\tt Int64}, default: 100): order used for all Gauss quadratures (denoted by $N$ below). + +\end{itemize} +\bigskip + +The available {\tt commands} are the following. + +\begin{itemize} + \item {\tt rho\_e}\makelink{command:simpleq-iteration_rho_e}{}: + compute the density $\rho$ as a function of $e$.\par + \underline{Disabled parameters}: \refname{param:simpleq-iteration_e}{{\tt e}}.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:simpleq-iteration_minle}{} + {\tt minle} ({\tt Float64}, default: $10^{-6}$): minimal value for $\log_{10}e$. + + \item\makelink{param:simpleq-iteration_maxle}{} + {\tt maxle} ({\tt Float64}, default: $10^{2}$): maximal value for $\log_{10}e$. + + \item\makelink{param:simpleq-iteration_nle}{} + {\tt nle} ({\tt Int64}, default: $100$): number of values for $e$ (spaced logarithmically). + + \item\makelink{param:simpleq-iteration_es}{} + {\tt es} ({\tt Array\{Float64\}}, default: $(10^{{\tt minle}+\frac{{\tt maxle}-{\tt minle}}{{\tt nle}}n})_n$: list of values for $e$, specified as a `{\tt,}' separated list. + \end{itemize} + \underline{Output} (one line for each value of $e$): [$e$] [$\rho$]. + + \item\makelink{command:simpleq-iteration_ux}{} + {\tt ux}: compute $u$ as a function of $|x|$.\par + \underline{Extra parameters}: + \begin{itemize} + \item\makelink{param:simpleq-iteration_xmin}{} + {\tt xmin} ({\tt Float64}, default: 0): minimum of the range of $|x|$ to be printed. + + \item\makelink{param:simpleq-iteration_xmax}{} + {\tt xmax} ({\tt Float64}, default: 100): maximum of the range of $|x|$ to be printed. + + \item\makelink{param:simpleq-iteration_nx}{} + {\tt nx} ({\tt Int64}, default: 100): number of points to print (linearly spaced). + \end{itemize} + \underline{Output} (one line for each value of $x$): [$|x|$] [$u_1(|x|)$] [$u_2(|x|)$] [$u_3(|x|)$] $\cdots$ + +\end{itemize} + +\subsubsection{Description} +\indent +In Fourier space +\begin{equation} + \hat u_n(|k|):=\int dx\ e^{ikx}u_n(|x|) +\end{equation} +(\ref{iteration}) becomes +\begin{equation} + (k^2+4e)\hat u_n(|k|)=\hat S_n(|k|)+2e\rho_{n-1}\hat u_{n-1}(k)^2 + ,\quad + \rho_n:=\frac{2e}{\hat S_n(0)} + \label{iteration_uk} +\end{equation} +with +\begin{equation} + \hat S_n(|k|) + =\hat v(|k|)-\frac1{(2\pi)^3}\int dp\ \hat u_n(|p|)\hat v(|k-p|) + . +\end{equation} +We write $\hat S_n$ in bipolar coordinates (see lemma\-~\ref{lemma:bipolar}): +\begin{equation} + \hat S_n(|k|)=\hat v(|k|)-\frac1{(2\pi)^3}\int_0^\infty dt\ t\hat u_n(t)\Eta(|k|,t) +\end{equation} +with +\begin{equation} + \Eta(y,t):=\frac{2\pi}y\int_{|y-t|}^{y+t}ds\ s\hat v(s) +\end{equation} +(note that this agrees with the function\-~(\ref{easyeq_Eta}) defined for \refname{sec:easyeq}{{\tt easyeq}}). +We also change variables to +\begin{equation} + y=\frac1{t+1} + ,\quad + t=\frac{1-y}y +\end{equation} +\begin{equation} + \hat S_n(|k|)=\hat v(|k|)-\frac1{(2\pi)^3}\int_0^1 dy\ \frac{(1-y)\hat u_n(\frac{1-y}y)\Eta(|k|,\frac{1-y}y)}{y^3} + . +\end{equation} +We approximate this integral using a Gauss-Legendre quadrature (see appendix\-~\ref{appGL}) and discretize Fourier space: +\begin{equation} + k_i:=\frac{1-x_i}{1+x_i} + ,\quad + y_i:=\frac{x_i+1}2 +\end{equation} +where $x_i$ are the Gauss-Legendre abscissa, and +\begin{equation} + \hat S_n(k_i)\approx\hat v(k_i)-\frac1{2(2\pi)^3}\sum_{j=1}^Nw_j\frac{(1-y_j)\hat u_n(\frac{1-y_j}{y_j})\Eta(k_i,\frac{1-y_j}{y_j})}{y_j^3} +\end{equation} +so if +\begin{equation} + \mathbb U_i(n):=\hat u_n(k_i) + ,\quad + \mathbf v_i:=\hat v(k_i) +\end{equation} +we have +\begin{equation} + \sum_{j=1}^NA_{i,j}\mathbb U_j(n)=b_i^{(n)} +\end{equation} +with +\begin{equation} + A_{i,j}:=(k_i^2+4e)\delta_{i,j}+\frac{w_j(1-y_j)\Eta(k_i,\frac{1-y_j}{y_j})}{2(2\pi)^3y_j^3} +\end{equation} +and +\begin{equation} + b_i^{(n)}:=\mathbf v_i+2e\rho_{n-1}\mathbb U_i(n-1)^2 +\end{equation} +in terms of which +\begin{equation} + \mathbb U=A^{-1}b^{(n)} + . +\end{equation} +Finally, we compute $\rho_n$ using the second of\-~(\ref{iteration_uk}): +\begin{equation} + \rho_n=\frac{2e}{\hat S_n(0)} + ,\quad + S_n(0)\approx\hat v(0) + -\frac1{2(2\pi)^3}\sum_{j=1}^Nw_j\frac{(1-y_j)\hat u_n(\frac{1-y_j}{y_j})\Eta(0,\frac{1-y_j}{y_j})}{y_j^3} + . +\end{equation} + +\section{Potentials}\label{sec:potentials} +\indent +In this section, we describe the potentials available in {\tt simplesolv}, and provide documentation to add custom potentials to {\tt simplesolv}. +\bigskip + +\subsection{Built-in potentials} +\subsubsection{{\tt exp}}\label{sec:exp} +\indent +In $x$-space, +\begin{equation} + v(|x|)=ae^{-|x|} + . +\end{equation} +The constant $a$ is specified through the {\tt v\_a} parameter, and can be any real number. +Note that $v\geqslant 0$ if and only if $a\geqslant 0$. +This is the potential that is selected by default. +\bigskip + +\point +In Fourier space, +\begin{equation} + \hat v(|k|)=\int dx\ e^{ikx}v(|x|) + =\frac{8\pi a}{(1+k^2)^2} + . +\end{equation} +In particular, $v$ is of positive type (that is, $\hat v\geqslant 0$) if and only if $a\geqslant 0$. +\bigskip + +\point +The zero energy scattering solution, that is, the solution of +\begin{equation} + (-\Delta+v)\psi=0 + ,\quad + \lim_{|x|\to\infty}\psi=1 +\end{equation} +is +\begin{equation} + \psi(r)=\frac{cI_0(2\sqrt a e^{-\frac r2})+2K_0(2\sqrt a e^{-\frac r2})}r + ,\quad + c=-\frac{2K_0(2\sqrt a)}{I_0(2\sqrt a)} +\end{equation} +where $I_0$ and $K_0$ are the modified Bessel functions, and the square root is taken with a branch cut on the negative imaginary axis. +In other words, if $a<0$, $\sqrt a=i\sqrt{|a|}$ and\-~\dlmfcite{(10.27.6),(10.27.9),(10.27.10)}{1.1.3} +\begin{equation} + I_0(ix)=J_0(x) + ,\quad + K_0(ix)=-\frac\pi2(Y_0(x)+iJ_0(x)) +\end{equation} +where $J_0$ and $Y_0$ are the Bessel functions, so +\begin{equation} + \psi(r)=\pi\frac{c'J_0(2\sqrt{|a|} e^{-\frac r2})-Y_0(2\sqrt{|a|} e^{-\frac r2})}r + ,\quad + c'=\frac{Y_0(2\sqrt{|a|})}{J_0(2\sqrt{|a|})} + . +\end{equation} +The scattering length is\-~\dlmfcite{(10.25.2),(10.31.2)}{1.1.3} +\begin{equation} + \mathfrak a_0=\lim_{r\to\infty}r(1-\psi(r)) + =\log(a)+2\gamma+\frac{2K_0(2\sqrt a)}{I_0(2\sqrt a)} +\end{equation} +where $\gamma$ is the Euler constant, which, for $a<0$, is +\begin{equation} + \mathfrak a_0=\log|a|+2\gamma-\frac{\pi Y_0(2\sqrt{|a|})}{J_0(2\sqrt{|a|})} + . +\end{equation} + +\subsubsection{{\tt tent}}\label{sec:tent} +\indent +In $x$-space, +\begin{equation} + v(|x|)=\mathds 1_{|x|0$. +Note that $v\geqslant 0$ if and only if $a\geqslant 0$. +Note that +\begin{equation} + v(|x|)=\frac a{b^3}\mathds 1_{|x|<\frac b2}\ast\mathds 1_{|x|<\frac b2} +\end{equation} +(this is more easily checked in Fourier space than explicitly computing the convolution). +\bigskip + +\indent +In Fourier space, +\begin{equation} + \hat v(|k|)=\int dx\ e^{ikx}v(|x|)=a\frac{b^3}8\left(4\pi\frac{\sin(\frac{|k|b}2)-\frac{|k|b}2\cos(\frac{|k|b}2)}{\frac{(|k|b)^3}8}\right)^2 + . +\end{equation} +Note that this is $\frac a{b^3}(\hat\mathds 1_{|x|<\frac b2})^2$. +In particular, $v$ is of positive type (that is, $\hat v\geqslant 0$) if and only if $a\geqslant 0$. + +\subsubsection{{\tt expcry}}\label{sec:expcry} +\indent +In $x$-space +\begin{equation} + v(|x|)=e^{-|x|}-ae^{-b|x|} + . +\end{equation} +The constants $a$ and $b$ are specified through the {\tt v\_a} and {\tt v\_b} parameters, and $a\in\mathbb R$, $b>0$. +Note that $v\geqslant 0$ if and only if $a\leqslant 1$ and $b\geqslant 1$. +\bigskip + +\indent +In Fourier space +\begin{equation} + \hat v(|k|)=\int dx\ e^{ikx}v(|x|) + =8\pi\left(\frac1{(1+k^2)^2}-\frac{ab}{(b^2+k^2)^2}\right) +\end{equation} +In particular, $\hat v$ is of positive type (that is, $\hat v\geqslant 0$) if and only if $ab\leqslant 1$, $a\leqslant b$ and $a\leqslant b^3$. +If $a\leqslant 1$, $b\geqslant 1$ and $ab>1$, then $\hat v$ has a unique minimum at +\begin{equation} + |k_*|=\sqrt{\frac{b^2-(ab)^{\frac13}}{(ab)^{\frac13}-1}} + ,\quad + \hat v(|k_*|)= + -8\pi\frac{((ab)^{\frac13}-1)^3}{b^2-1} + . +\end{equation} + +\subsubsection{{\tt npt}}\label{sec:npt} +\indent +In $x$-space +\begin{equation} + v(|x|)=x^2e^{-|x|} + . +\end{equation} +Note that $v\geqslant 0$. +\bigskip + +\indent +In Fourier space +\begin{equation} + \hat v(|k|)=\int dx\ e^{ikx}v(|x|) + =96\pi\frac{1-k^2}{(1+k^2)^4} + . +\end{equation} +In particular, $v$ is not of positive type (that is, $\hat v$ is not $\geqslant 0$). + +\subsubsection{{\tt alg}}\label{sec:alg} +\indent +In $x$-space +\begin{equation} + v(|x|)=\frac1{1+\frac14|x|^4} + . +\end{equation} +Note that $v\geqslant 0$. +\bigskip + +\indent +In Fourier space +\begin{equation} + \hat v(|k|)=\int dx\ e^{ikx}v(|x|) + =4\pi^2 e^{-|k|}\frac{\sin(|k|)}{|k|} + . +\end{equation} +In particular, $v$ is not of positive type (that is, $\hat v$ is not $\geqslant 0$). + +\subsubsection{{\tt algwell}}\label{sec:algwell} +\indent +In $x$-space +\begin{equation} + v(|x|)=\frac{1+a|x|^4}{(1+|x|^2)^4} + . +\end{equation} +The constant $a$ can be set using the parameter {\tt v\_a} and can be any real number. +Note that $v\geqslant 0$ if and only if $a\geqslant 0$. +If $a>8$, this potential has a local minimum at $|x_-|$ and a local maximum at $|x_+|$: +\begin{equation} + |x_\pm|=\sqrt{\frac12(1\pm\sqrt{1-8a^{-1}})} + . +\end{equation} +\bigskip + +\indent +In Fourier space +\begin{equation} + \hat v(|k|)=\int dx\ e^{ikx}v(|x|) + =\frac{\pi^2}{24}e^{-|k|}(a(k^2-9|k|+15)+k^2+3|k|+3) + . +\end{equation} +In particular, $v$ is of positive type (that is, $\hat v$ is not $\geqslant 0$) if and only if +\begin{equation} + -\frac15\leqslant a\leqslant 3+\frac87\sqrt7\approx6.02 + . +\end{equation} + +\subsubsection{{\tt exact}}\label{sec:exact} +\indent +In $x$-space +\begin{equation} + v(|x|)= + \frac{12c( x^6b^6(2e-b^2) +b^4x^4(9e-7b^2) +4b^2x^2(3e-2b^2) +(5e+16b^2))}{(1+b^2x^2)^2(4+b^2x^2)^2((1+b^2x^2)^2-c)} +\end{equation} +The constants $a,b,c,e$ can be set using the parameters {\tt v\_a}, {\tt v\_b}, {\tt v\_c}, {\tt v\_e}, and $a,e\in\mathbb R$, $b\neq0$, $c>0$, $c\neq9$. +Note that $v\geqslant 0$ if and only if\-~\cite{CJL20b} +\begin{equation} + b>0 + ,\quad + 0{\rm \{potential function\}}(k,v\_param\_a,v\_param\_b,{\rm...})\par + \indent a0={\rm \{scattering length function\}}(v\_param\_a,v\_param\_b,{\rm...})\par +\end{code} +where the number of {\tt v\_param} entries should be the number of parameters of the potential. +The parameters that are currently read from the parameters list are {\tt a}, {\tt b}, {\tt c} and {\tt e}. +To add a parameter, it must first be declared and initialized after line 35, and code to read it should be added after line 172: +\begin{code} + elseif lhs="v\_{\rm\{name of parameter\}}"\par + \indent v\_param\_{\rm\{name of parameter\}}=parse(Float64,rhs) +\end{code} +If the new parameter has a type other than {\tt Float64}, this should be changed in the {\tt parse} function, and in the initialization. +\bigskip + +\indent +The approximation of the scattering length is only used to initialize the Newton algorithm for \refname{sec:easyeq}{{\tt easyeq}}, so it is not important that it be exact. +In fact, some of the built-in potentials set the scattering length to 1, when it has proved too difficult to compute it exactly. + +\appendix + +\section{Chebyshev polynomial expansion}\label{appChebyshev} +\indent +In this appendix, we compute the error of Chebyshev polynomial expansions of regular functions. +Specifically, we will consider class-$s$ Gevrey functions (which is a generalization of the notion of analyticity: analytic functions are Gevrey functions with $s=1$). +A class-$s$ Gevrey function on $[-1,1]$ is a $\mathcal C^\infty$ function that satisfies, $\forall n\in\mathbb N$, +\begin{equation} + \mathop{\mathrm{sup}}_{x\in[-1,1]}\left|\frac{d^nf(x)}{dx^n}\right|\leqslant C_0C^n(n!)^s. +\end{equation} +Formally, the Chebyshev polynomial expansion of $f$ is +\begin{equation} + f(x)=\frac{c_0}2+\sum_{j=1}^\infty c_j T_j(x) + \label{eqcheby} +\end{equation} +where $T_j$ is the $j$-th Chebyshev polynomial: +\begin{equation} + T_j(x):=\cos(j\arccos(x)) +\end{equation} +and +\begin{equation} + c_j:=\frac2\pi\int_0^\pi d\theta\ f(\cos\theta)\cos(j\theta) + . +\end{equation} + +\bigskip + +\theo{Lemma}\label{lemmaChebyshev} + Let $f$ be a class-$s$ Gevrey function on $[-1,1]$ with $s\in\mathbb N\setminus\{0\}$. + There exist $b_0,b>0$, which are independent of $s$, such that the coefficients $c_j$ of the Chebyshev polynomial expansion are bounded as + \begin{equation} + c_j\leqslant b_0e^{-bj^{\frac1s}} + . + \label{boundchebyshevcoef} + \end{equation} + In particular, the Chebyshev polynomial expansion is absolutely convergent pointwise (and, therefore in any $L_p$ norm), and, for every $N\geqslant 1$, + \begin{equation} + \left|f(x)-\frac{c_0}2-\sum_{j=1}^N c_j T_j(x)\right| + \leqslant + \frac{b_0}{1-e^{-b}}(s-1)!(N^{\frac1s}+b^{-1})^{s-1}e^{-bN^{\frac1s}} + . + \label{boundchebyshev} + \end{equation} +\endtheo +\bigskip + +\indent\underline{Proof}: + Note that~\-(\ref{eqcheby}) is nothing other than the Fourier cosine series expansion of $F(\theta):=f(\cos(\theta))$, which is an even, periodic, class-$s$ Gevrey function on $[-\pi,\pi]$, whose $j$-th Fourier coefficient for $j\in\mathbb Z$ is equal to $\frac12c_{|j|}$. + The bound\-~(\ref{boundchebyshevcoef}) follows from a well-known estimate of the decay of Fourier coefficients of class-$s$ Gevrey functions (see e.g.~\-\cite[Theorem~\-3.3]{Ta87}). + The bound\-~(\ref{boundchebyshev}) then follows from $|T_j(x)|\leqslant 1$ and lemma\-~\ref{lemma:ineqgevrey} below. +\qed +\bigskip + +\theo{Lemma}\label{lemma:ineqgevrey} + Given $b>0$ and two integers $N,s>0$, + \nopagebreakaftereq + \begin{equation} + \sum_{j=N}^\infty e^{-bj^{\frac1s}} + \leqslant + (s-1)!\left(N^{\frac1s}+\frac1{1-e^{-b}}\right)^{s-1}\frac{e^{-bN^{\frac1s}}}{1-e^{-b}} + \end{equation} +\endtheo +\restorepagebreakaftereq +\bigskip + +\indent\underline{Proof}: + If $\nu_{N,s}^s:=\lfloor N^{\frac1s}\rfloor^s$ denotes the largest integer that is $\leqslant N$ and has an integer $s$-th root, then + \begin{equation} + \sum_{j=N}^\infty e^{-bj^{\frac1s}}\leqslant + \sum_{j=\nu_{N,s}^s}^\infty e^{-bj^{\frac1s}}\leqslant + \sum_{k=\nu_{N,s}}^\infty(k^s-(k-1)^s)e^{-bk}\leqslant + s\sum_{k=\nu_{N,s}}^\infty k^{s-1}e^{-bk} + . + \end{equation} + We then estimate + \begin{equation} + \sum_{k=\nu_{N,s}}^\infty k^{s-1}e^{-bk}=\frac{d^{s-1}}{d(-b)^{s-1}}\sum_{k=\nu_{N,s}}^\infty e^{-bk} + \leqslant (s-1)!\left(\nu_{N,s}+\frac1{1-e^{-b}}\right)^{s-1}\frac{e^{-b\nu_{N,s}}}{1-e^{-b}} + \end{equation} + which concludes the proof of the lemma. +\qed + +\section{Gauss quadratures}\label{appGL} +\indent +Gauss quadratures are approximation schemes to compute integrals of the form +\begin{equation} + \int_a^bdt\ \omega(t)f(t) +\end{equation} +where $\omega(x)\geqslant 0$ is one of several functions that Gauss quadratures can treat. +The possible choices of $\omega$ and $(a,b)$ are +\begin{itemize} + \item $(a,b)=(-1,1)$, $\omega(t)=1$: Gauss-Legendre quadratures. + \item $(a,b)=(-1,1)$, $\omega(t)=(1-t)^\alpha(1+t)^\beta$, $\alpha,\beta>-1$: Gauss-Jacobi quadratures. + \item $(a,b)=(-1,1)$, $\omega(t)=(1-t^2)^{-\frac12}$: Gauss-Chebyshev quadratures. + \item $(a,b)=(0,\infty)$, $\omega(t)=e^{-t}$: Gauss-Laguerre quadratures. + \item $(a,b)=(0,\infty)$, $\omega(t)=t^\alpha e^{-t}$, $\alpha>-1$: generalized Gauss-Laguerre quadratures. + \item $(a,b)=(0,\infty)$, $\omega(t)=e^{-t^2}$: Gauss-Hermite quadratures. +\end{itemize} +It is not our goal here to discuss Gauss quadratures in detail, or their relation to orthogonal polynomials. +Instead, we will compute the error made when approximating such an integral by a Gauss quadrature. +\bigskip + +\indent +For each Gauss quadrature, the integral is approximated in the form +\begin{equation} + \int_a^b dt\ \omega(t) f(t) + \approx + \sum_{i=1}^Nw_if(r_i) +\end{equation} +where $w_i$ are called the {\it weights}, $r_i$ are the {\it abcissa}, and $N$ is the {\it order}. +The weights and abcissa depend on both $\omega$ and the order $N$. +The crucial property of Gauss quadratures is that they are {\it exact} when $f$ is a polynomial of order $\leqslant 2N-1$. +\bigskip + +\indent +In this appendix, we compute the error of Gauss quadratures when used to integrate regular functions. +Specifically, we will consider class-$s$ Gevrey functions (which is a generalization of the notion of analyticity: analytic functions are Gevrey functions with $s=1$). +A class-$s$ Gevrey function on is a $\mathcal C^\infty$ function that satisfies, $\forall n\in\mathbb N$, +\begin{equation} + \mathop{\mathrm{sup}}_{x\in[-1,1]}\left|\frac{d^nf(x)}{dx^n}\right|\leqslant C_0C^n(n!)^s. +\end{equation} + +\bigskip + +\theo{Lemma}\label{lemmaGL} + Let $f$ be a class-$s$ Gevrey function with $s\in\mathbb N\setminus\{0\}$. + There exist $b_0,b>0$, which are independent of $s$, and $N_0>0$, which is independent of $s$ and $f$, such that, if $N\geqslant N_0$, then, denoting the Gauss weights and abcissa by $w_i$ and $r_i$, + \begin{equation} + \left|\int_a^bdt\ \omega(t)f(t)-\sum_{i=1}^Nw_if(r_i)\right| + \leqslant + b_0(s-1)!((2N-1)^{\frac1s}+b^{-1})^{s-1}e^{-b(2N-1)^{\frac1s}}\left(\int_a^b\omega(t)+\sum_{i=1}^Nw_i\right) + . + \end{equation} + In particular, if $f$ is analytic (i.e. $s=1$), then + \begin{equation} + \left|\int_a^bdt\ \omega(t)f(t)-\sum_{i=1}^Nw_if(r_i)\right| + \leqslant b_0e^{-b(2N-1)}\left(\int_a^b\omega(t)+\sum_{i=1}^Nw_i\right) + . + \end{equation} +\endtheo +\bigskip + +\indent\underline{Proof}: + We expand $f$ into Chebyshev polynomials as in\-~(\ref{eqcheby}): + \begin{equation} + f(x)=\frac{c_0}2+\sum_{j=1}^{\infty}c_jT_j(x) + \end{equation} + Let + \begin{equation} + g(x):=f(x)-\frac{c_0}2-\sum_{j=1}^{2N-1} c_j T_j(x) + . + \end{equation} + Since order-$N$ Gauss quadratures are exact on polynomials of order $\leqslant 2N-1$, we have + \begin{equation} + \int_a^bdt\ \omega(t)f(t)-\sum_{i=1}^Nw_if(r_i) + = + \int_a^bdt\ \omega(t)g(t)-\sum_{i=1}^Nw_ig(r_i) + \end{equation} + and, by lemma\-~\ref{lemmaChebyshev}, + \begin{equation} + |g(x)|\leqslant + (\mathrm{const}.)(s-1)!((2N-1)^{\frac1s}+b^{-1})^{s-1}e^{-b(2N-1)^{\frac1s}} + . + \label{boundchebyshev} + \end{equation} +\qed + +\section{Bipolar coordinates}\label{appendix:bipolar} +\indent +Bipolar coordinates are very useful for computing convolutions of radial functions in three dimensions. +\bigskip + +\theo{Lemma}\label{lemma:bipolar} + For $y\in\mathbb R^3$, + \nopagebreakaftereq + \begin{equation} + \int_{\mathbb R^3}dx\ f(|x|,|x-y|) + = + \frac{2\pi}{|y|}\int_0^\infty dt\int_{||y|-t|}^{|y|+t}ds\ stf(s,t) + \end{equation} +\endtheo +\restorepagebreakaftereq + +\indent\underline{Proof}: + Without loss of generality, we assume that $y=(0,0,a)$ with $a>0$. We first change to cylindrical coordinates: $(\rho,\theta,x_3)$: + \begin{equation} + \int_{\mathbb R^3}dx\ f(|x|,|x-y|) + = + 2\pi\int_0^\infty d\rho\int_{-\infty}^\infty dx_3\ \rho f(|(\rho,0,x_3)|,|(\rho,0,x_3-a)|) + . + \end{equation} + Next, we change variables to + \begin{equation} + s=|(\rho,0,x_3)| + ,\quad + t=|(\rho,0,x_3-a)| + . + \end{equation} + The inverse of this transformation is + \begin{equation} + x_3=\pm\frac{a^2+s^2-t^2}{2a} + ,\quad + \rho=\frac1{2a}\sqrt{4a^2s^2-(a^2+s^2-t^2)^2} + \label{inverse} + \end{equation} + and its Jacobian is + \begin{equation} + \frac{2ts}{\sqrt{-2a^4+2a^2(t^2+s^2)-(t^2-s^2)^2}} + =\frac{ts}{\rho a} + . + \label{jacobian} + \end{equation} +\qed +\bigskip + +\indent +The following is a generalization of the previous lemma to functions of four variables. +\bigskip + +\theo{Lemma}\label{lemma:bipolar2} + For $y\in\mathbb R^3$, + \begin{equation} + \begin{largearray} + \int_{\mathbb R^6}dxdx'\ f(|x|,|x-y|,|x'|,|x'-y|,|x-x'|) + \\[0.5cm]\hfill + = + \frac{2\pi}{|y|^2}\int_0^\infty dt\int_{||y|-t|}^{|y|+t}ds\int_0^\infty dt'\int_{||y|-t'|}^{|y|+t'}ds'\int_0^{2\pi}d\theta\ sts't'f(s,t,s',t',\xi(s,t,s',t',\theta,|y|)) + \end{largearray} + \end{equation} + with + \nopagebreakaftereq + \begin{equation} + \begin{largearray} + \xi(s,t,s',t',\theta,|y|) + := + \frac1{\sqrt 2|y|} + \Big( + |y|^2(s^2+t^2+(s')^2+(t')^2)-|y|^4-(s^2-t^2)((s')^2-(t')^2) + \\[0.5cm]\hfill + - + \sqrt{(4|y|^2s^2-(|y|^2+s^2-t^2)^2)(4|y|^2(s')^2-(|y|^2+(s')^2-(t')^2)^2)}\cos\theta + \Big)^{\frac12} + . + \end{largearray} + \end{equation} +\endtheo +\restorepagebreakaftereq + +\indent\underline{Proof}: + Without loss of generality, we assume that $y=(0,0,a)$ with $a>0$. We first change to cylindrical coordinates: $(\rho,\theta,x_3;\rho',\theta',x_3')$: + \begin{equation} + \begin{largearray} + \int_{\mathbb R^6}dxdx'\ f(|x|,|x-y|,|x'|,|x'-y|,|x-x'|) + \\[0.5cm]\hfill + = + 2\pi\int_0^\infty d\rho\int_{-\infty}^\infty dx_3 + \int_0^\infty d\rho'\int_{-\infty}^\infty dx_3'\int_0^{2\pi}d\theta' + \ \rho\rho' + f( + s,t,s',t', + |(\rho-\rho'\cos\theta',\rho'\sin\theta',x_3-x_3')| + ) + . + \end{largearray} + \end{equation} + where + \begin{equation} + s:=|(\rho,0,x_3)| + ,\quad + t:=|(\rho,0,x_3-a)| + ,\quad + s':=|(\rho'\cos\theta',\rho'\sin\theta',x_3')|, + t':=|(\rho'\cos\theta',\rho'\sin\theta',x_3'-a)| + \end{equation} + Next, we change variables to $(s,t,s',t',\theta')$. + The Jacobian of this transformation is, by\-~(\ref{jacobian}), + \begin{equation} + \frac{tst's'}{\rho\rho' a^2} + . + \end{equation} + Furthermore, by\-~(\ref{inverse}), + \begin{equation} + x_3-x_3'=\frac{s^2-t^2-(s')^2+(t')^2}{2a} + \end{equation} + and + \begin{equation} + \rho=\frac{\sqrt{4a^2s^2-(a^2+s^2-t^2)^2}}{2a} + ,\quad + \rho'=\frac{\sqrt{4a^2(s')^2-(a^2+(s')^2-(t')^2)^2}}{2a} + \end{equation} + so + \begin{equation} + \begin{largearray} + |(\rho-\rho'\cos\theta',\rho'\sin\theta',x_3-x_3')| + = + \frac1{2a} + \left( + 4a^2s^2-(a^2+s^2-t^2)^2 + + + 4a^2(s')^2-(a^2+(s')^2-(t')^2)^2 + \right.\\[0.5cm]\hfill\left. + - + 2\sqrt{(4a^2s^2-(a^2+s^2-t^2)^2)(4a^2(s')^2-(a^2+(s')^2-(t')^2)^2)}\cos\theta' + + + (s^2-t^2-(s')^2+(t')^2)^2 + \right)^{\frac12} + \end{largearray} + \end{equation} + and + \begin{equation} + \begin{largearray} + |(\rho-\rho'\cos\theta',\rho'\sin\theta',x_3-x_3')| + = + \frac1{\sqrt 2a} + \Big( + a^2(s^2+t^2+(s')^2+(t')^2)-a^4-(s^2-t^2)((s')^2-(t')^2) + \\[0.5cm]\hfill + - + \sqrt{(4a^2s^2-(a^2+s^2-t^2)^2)(4a^2(s')^2-(a^2+(s')^2-(t')^2)^2)}\cos\theta' + \Big)^{\frac12} + . + \end{largearray} + \end{equation} +\qed + +\section{Hann windowing}\label{appendix:hann} +\indent +In this appendix, we discuss the use of Hann windows to compute Fourier transforms. +Consider the Fourier transform +\begin{equation} + \hat f(k)=\int dx\ e^{ikx}f(x) + . +\end{equation} +Evaluating this integral numerically can be tricky, especially at high $|k|$, because of the rapid oscillations at large $|x|$. +A trick to palliate such a problem is to multiply $f$ by a {\it window function} $h_L$, which cuts off distances of order $L$. +We then compute, instead of $\hat f$, +\begin{equation} + \tilde f(k)=\int dx\ e^{ikx}h_L(x)f(x) + . +\end{equation} +We can then evaluate $\tilde f$ using standard numerical techniques, such as Gauss quadratures (see appendix\-~\ref{appGL}), without issues at large $|x|$. +However, in doing so, we will make an error in the Fourier transform. +To quantify this error, note that +\begin{equation} + \tilde f(k) + =\hat h_L\hat\ast\hat f(k) + \equiv\int\frac{dq}{(2\pi)^d}\ \hat h_L(q)\hat f(k-q) +\end{equation} +so if we choose $h_L$ in such a way that $\hat h_L$ is peaked around the origin, then $\tilde f$ will not differ too much from $\hat f$: +\begin{equation} + \hat f(k)-\tilde f(k) + =((2\pi)^d\delta(k)-\hat h_L)\hat\ast\hat f(k) + . +\end{equation} +\bigskip + +\indent +The {\it Hann} window is defined as +\begin{equation} + h_L(x)=\cos^2({\textstyle\frac{\pi|x|}L})\mathds 1_{|x|<\frac L2} +\end{equation} +whose Fourier transform is, in $d=3$, +\begin{equation} + \hat h_L(k)= + \frac{4\pi^3 L^2}{|k|}\frac{((|k|L)^3-4|k|L\pi^2)\cos(\frac{|k|L}2)-2(3(|k|L)^2-4\pi^2)\sin(\frac{|k|L}2)}{((|k|L)^3-4|k|L\pi^2)^2} +\end{equation} +which decays at large $|k|L$ as +\begin{equation} + \hat h_L(k)\sim\frac{4\pi^3}{|k|^4L}\cos({\textstyle\frac{|k|L}2}) + . +\end{equation} + + + +\vfill +\eject + +\begin{thebibliography}{WWW99} +\small +\IfFileExists{bibliography/bibliography.tex}{\input bibliography/bibliography.tex}{} +\end{thebibliography} + + +\end{document} diff --git a/src/anyeq.jl b/src/anyeq.jl new file mode 100644 index 0000000..8459cb0 --- /dev/null +++ b/src/anyeq.jl @@ -0,0 +1,949 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# interpolation +@everywhere mutable struct Anyeq_approx + aK::Float64 + bK::Float64 + gK::Float64 + aL1::Float64 + bL1::Float64 + aL2::Float64 + bL2::Float64 + gL2::Float64 + aL3::Float64 + bL3::Float64 + gL3::Float64 +end + +# compute energy for a given rho +# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm) +function anyeq_energy(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile) + # initialize vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + + @printf("% .15e % .15e\n",E,error) +end + +# compute energy as a function of rho +function anyeq_energy_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile) + # initialize vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + + # save result from each task + es=Array{Float64,1}(undef,length(rhos)) + err=Array{Float64,1}(undef,length(rhos)) + + ## spawn workers + # number of workers + nw=nworkers() + # split jobs among workers + work=Array{Array{Int64,1},1}(undef,nw) + # init empty arrays + for p in 1:nw + work[p]=zeros(0) + end + for j in 1:length(rhos) + append!(work[(j-1)%nw+1],j) + end + + count=0 + # for each worker + @sync for p in 1:nw + # for each task + @async for j in work[p] + count=count+1 + if count>=nw + progress(count,length(rhos),10000) + end + # run the task + (u,es[j],err[j])=remotecall_fetch(anyeq_hatu,workers()[p],u0s[j],P,N,J,rhos[j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + end + end + + for j in 1:length(rhos) + @printf("% .15e % .15e % .15e\n",rhos[j],es[j],err[j]) + end +end + +# compute energy as a function of rho +# initialize with previous rho +function anyeq_energy_rho_init_prevrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile) + # initialize vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + u0s=anyeq_init_medeq([rhos[1]],N,J,k,a0,v,maxiter,tolerance) + u=u0s[1] + + for j in 1:length(rhos) + progress(j,length(rhos),10000) + # run the task + # init Newton from previous rho + (u,E,error)=anyeq_hatu(u,P,N,J,rhos[j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + @printf("% .15e % .15e % .15e\n",rhos[j],E,error) + # abort when the error gets too big + if error>tolerance + break + end + end +end +# compute energy as a function of rho +# initialize with next rho +function anyeq_energy_rho_init_nextrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile) + # initialize vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + u0s=anyeq_init_medeq([rhos[length(rhos)]],N,J,k,a0,v,maxiter,tolerance) + u=u0s[1] + + for j in 1:length(rhos) + progress(j,length(rhos),10000) + # run the task + # init Newton from previous rho + (u,E,error)=anyeq_hatu(u,P,N,J,rhos[length(rhos)+1-j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + @printf("% .15e % .15e % .15e\n",rhos[length(rhos)+1-j],real(E),error) + # abort when the error gets too big + if error>tolerance + break + end + end +end + +# compute u(k) +# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm) +function anyeq_uk(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,approx,savefile) + # init vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + + for zeta in 0:J-1 + for j in 1:N + # order k's in increasing order + @printf("% .15e % .15e\n",k[(J-1-zeta)*N+j],u[(J-1-zeta)*N+j]) + end + end +end + +# compute u(x) +# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm) +function anyeq_ux(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx,savefile) + # init vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + + for i in 1:nx + x=xmin+(xmax-xmin)*i/nx + ux=0. + for zeta in 0:J-1 + for j in 1:N + ux+=(taus[zeta+2]-taus[zeta+1])/(16*pi*x)*weights[2][j]*cos(pi*weights[1][j]/2)*(1+k[zeta*N+j])^2*k[zeta*N+j]*u[zeta*N+j]*sin(k[zeta*N+j]*x) + end + end + @printf("% .15e % .15e % .15e\n",x,real(ux),imag(ux)) + end +end + +# compute condensate fraction for a given rho +# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm) +function anyeq_condensate_fraction(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile) + # initialize vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + + # compute eta + eta=anyeq_eta(u,P,N,J,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx) + + @printf("% .15e % .15e\n",eta,error) +end + +# condensate fraction as a function of rho +function anyeq_condensate_fraction_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile) + ## spawn workers + # number of workers + nw=nworkers() + # split jobs among workers + work=Array{Array{Int64,1},1}(undef,nw) + # init empty arrays + for p in 1:nw + work[p]=zeros(0) + end + for j in 1:length(rhos) + append!(work[(j-1)%nw+1],j) + end + + # initialize vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + + # compute u + us=Array{Array{Float64,1}}(undef,length(rhos)) + errs=Array{Float64,1}(undef,length(rhos)) + count=0 + # for each worker + @sync for p in 1:nw + # for each task + @async for j in work[p] + count=count+1 + if count>=nw + progress(count,length(rhos),10000) + end + # run the task + (us[j],E,errs[j])=remotecall_fetch(anyeq_hatu,workers()[p],u0s[j],P,N,J,rhos[j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + end + end + + # compute eta + etas=Array{Float64}(undef,length(rhos)) + count=0 + # for each worker + @sync for p in 1:nw + # for each task + @async for j in work[p] + count=count+1 + if count>=nw + progress(count,length(rhos),10000) + end + # run the task + etas[j]=anyeq_eta(us[j],P,N,J,rhos[j],weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx) + end + end + + for j in 1:length(rhos) + @printf("% .15e % .15e % .15e\n",rhos[j],etas[j],errs[j]) + end +end + +# compute the momentum distribution for a given rho +# use minlrho, nlrho to incrementally compute the solution to medeq (to initialize the Newton algorithm) +function anyeq_momentum_distribution(rho,minlrho,nlrho,taus,P,N,J,a0,v,maxiter,tolerance,approx,savefile) + # initialize vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + + # compute M + M=anyeq_momentum(u,P,N,J,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx) + + for zeta in 0:J-1 + for j in 1:N + # order k's in increasing order + @printf("% .15e % .15e\n",k[(J-1-zeta)*N+j],M[(J-1-zeta)*N+j]) + end + end +end + +# 2 point correlation function +function anyeq_2pt_correlation(minlrho,nlrho,taus,P,N,J,windowL,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx,savefile) + # init vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + # init Abar + if savefile!="" + Abar=anyeq_read_Abar(savefile,P,N,J) + else + Abar=anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + end + + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + # compute u and some useful integrals + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx) + + for i in 1:nx + x=xmin+(xmax-xmin)*i/nx + C2=anyeq_2pt(x,u,P,N,J,windowL,rho,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,approx,S,E,II,JJ,X,Y,sL1,sK,G) + @printf("% .15e % .15e\n",x,C2) + end +end + +# compute Abar +function anyeq_Abar_multithread(k,weights,taus,T,P,N,J,approx) + if approx.bL3==0. + return [] + end + + out=Array{Array{Float64,5}}(undef,J*N) + + ## spawn workers + # number of workers + nw=nworkers() + # split jobs among workers + work=Array{Array{Int64,1},1}(undef,nw) + # init empty arrays + for p in 1:nw + work[p]=zeros(0) + end + for j in 1:N*J + append!(work[(j-1)%nw+1],j) + end + + count=0 + # for each worker + @sync for p in 1:nw + # for each task + @async for j in work[p] + count=count+1 + if count>=nw + progress(count,N*J,10000) + end + # run the task + out[j]=remotecall_fetch(barAmat,workers()[p],k[j],weights,taus,T,P,N,J,2,2,2,2,2) + end + end + + return out +end + +# initialize computation +@everywhere function anyeq_init(taus,P,N,J,v) + # Gauss-Legendre weights + weights=gausslegendre(N) + + # initialize vectors V,k + V=Array{Float64}(undef,J*N) + k=Array{Float64}(undef,J*N) + for zeta in 0:J-1 + for j in 1:N + xj=weights[1][j] + # set kj + k[zeta*N+j]=(2+(taus[zeta+2]-taus[zeta+1])*sin(pi*xj/2)-(taus[zeta+2]+taus[zeta+1]))/(2-(taus[zeta+2]-taus[zeta+1])*sin(pi*xj/2)+taus[zeta+2]+taus[zeta+1]) + # set v + V[zeta*N+j]=v(k[zeta*N+j]) + end + end + # potential at 0 + V0=v(0) + + # initialize matrix A + T=chebyshev_polynomials(P) + A=Amat(k,weights,taus,T,P,N,J,2,2) + + # compute Upsilon + # Upsilonmat does not use splines, so increase precision + weights_plus=gausslegendre(N*J) + Upsilon=Upsilonmat(k,v,weights_plus) + Upsilon0=Upsilon0mat(k,v,weights_plus) + + return(weights,T,k,V,V0,A,Upsilon,Upsilon0) +end + +# compute initial guess from medeq +@everywhere function anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + us_medeq=Array{Array{Float64,1}}(undef,length(rhos)) + u0s=Array{Array{Float64,1}}(undef,length(rhos)) + + weights_medeq=gausslegendre(N*J) + + (us_medeq[1],E,err)=easyeq_hatu(easyeq_init_u(a0,J*N,weights_medeq),J*N,rhos[1],v,maxiter,tolerance,weights_medeq,Easyeq_approx(1.,1.)) + u0s[1]=easyeq_to_anyeq(us_medeq[1],weights_medeq,k,N,J) + if err>tolerance + print(stderr,"warning: computation of initial Ansatz failed for rho=",rhos[1],"\n") + end + + for j in 2:length(rhos) + (us_medeq[j],E,err)=easyeq_hatu(us_medeq[j-1],J*N,rhos[j],v,maxiter,tolerance,weights_medeq,Easyeq_approx(1.,1.)) + u0s[j]=easyeq_to_anyeq(us_medeq[j],weights_medeq,k,N,J) + + if err>tolerance + print(stderr,"warning: computation of initial Ansatz failed for rho=",rhos[j],"\n") + end + end + + return u0s +end + +# interpolate the solution of medeq to an input for anyeq +@everywhere function easyeq_to_anyeq(u_simple,weights,k,N,J) + # reorder u_simple, which is evaluated at (1-x_j)/(1+x_j) with x_j\in[-1,1] + u_s=zeros(Float64,length(u_simple)) + k_s=Array{Float64}(undef,length(u_simple)) + for j in 1:length(u_simple) + xj=weights[1][j] + k_s[length(u_simple)-j+1]=(1-xj)/(1+xj) + u_s[length(u_simple)-j+1]=u_simple[j] + end + + # initialize U + u=zeros(Float64,J*N) + for zeta in 0:J-1 + for j in 1:N + u[zeta*N+j]=linear_interpolation(k[zeta*N+j],k_s,u_s) + end + end + + return u +end + + +# compute u using chebyshev expansions +@everywhere function anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,approx) + # init + # rescale by rho (that's how u is defined) + u=rho*u0 + + # quantify relative error + error=-1. + + # run Newton algorithm + for i in 1:maxiter-1 + (S,E,II,JJ,X,Y,sL1,sK,G)=anyeq_SEIJGXY(u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx) + new=u-inv(anyeq_DXi(u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*anyeq_Xi(u,X,Y) + + error=norm(new-u)/norm(u) + if(error0 + dV[i]=sin(k[i]*x)/(k[i]*x)*hann(k[i],windowL) + else + dV[i]=hann(k[i],windowL) + end + end + dV0=1. + + # compute dUpsilon + # Upsilonmat does not use splines, so increase precision + weights_plus=gausslegendre(N*J) + dUpsilon=Upsilonmat(k,r->sin(r*x)/(r*x)*hann(r,windowL),weights_plus) + dUpsilon0=Upsilon0mat(k,r->sin(r*x)/(r*x)*hann(r,windowL),weights_plus) + + du=-inv(anyeq_DXi(rho*u,rho,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK))*anyeq_dXidv(x,rho*u,rho,k,taus,dV,dV0,A,Abar,dUpsilon,dUpsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK) + # rescale rho + du=du/rho + + C2=rho^2*(1-integrate_f_chebyshev(s->1.,u.*dV+V.*du,k,taus,weights,N,J)) + + return C2 +end + +# derivative of Xi with respect to v in the direction sin(kx)/kx +@everywhere function anyeq_dXidv(x,U,rho,k,taus,dv,dv0,A,Abar,dUpsilon,dUpsilon0,weights,P,N,J,approx,S,E,II,JJ,X,Y,sL1,sK) + # Chebyshev expansion of U + FU=chebyshev(U,taus,weights,P,N,J,2) + + dS=dv-conv_v_chebyshev(U,dUpsilon,k,taus,weights,N,J)/rho + dE=dv0-avg_v_chebyshev(U,dUpsilon0,k,taus,weights,N,J)/rho + + UU=conv_chebyshev(FU,FU,A) + + dII=zeros(Float64,N*J) + if approx.gL2!=0. + if approx.bL2!=0. + dII+=approx.gL2*approx.bL2*conv_chebyshev(FU,chebyshev(U.*dS,taus,weights,P,N,J,2),A)/rho + end + if approx.bL2!=1. + dII+=approx.gL2*(1-approx,bL2)*dE/rho*UU + end + end + + dJJ=zeros(Float64,J*N) + if approx.gL3!=0. + if approx.bL3!=0. + dFS=chebyshev(dS,taus,weights,P,N,J,2) + dJJ+=approx.gL3*approx.bL3*double_conv_S_chebyshev(FU,FU,FU,FU,dFS,Abar) + end + if approx.bL3!=1. + dJJ=approx.gL3*(1-approx.bL3)*dE*(UU/rho).^2 + end + end + + dG=zeros(Float64,N*J) + if approx.aK!=0. && approx.gK!=0. + if approx.bK!=0. + dG+=approx.aK*approx.gK*approx.bK*conv_chebyshev(FU,chebyshev(2*dS.*U,taus,weights,P,N,J,2),A)/rho + end + if approx.bK!=1. + dG+=approx.aK*approx.gK*(1-approx.bK)*2*dE*UU/rho + end + end + if approx.aL1!=0. + if approx.bL1!=0. + dG-=approx.aL1*approx.bL1*conv_chebyshev(FU,chebyshev(dS.*(U.^2),taus,weights,P,N,J,2),A)/rho + end + if approx.bL1!=1. + dG-=approx.aL1*(1-approx.bL1)*dE/rho*conv_chebyshev(FU,chebyshev((U.^2),taus,weights,P,N,J,2),A) + end + end + if approx.aL2!=0. && approx.gL2!=0. + dG+=approx.aL2*conv_chebyshev(FU,chebyshev(2*dII.*U,taus,weights,P,N,J,2),A)/rho + end + if approx.aL3!=0. && approx.gL3!=0. + dG-=approx.aL3*conv_chebyshev(FU,chebyshev(dJJ/2,taus,weights,P,N,J,2),A)/rho + end + + dsK=zeros(Float64,N*J) + if approx.gK!=0. + if approx.bK!=0. + dsK+=approx.gK*approx.bK*dS + end + if approx.bK!=1. + dsK+=approx.gK*(1-approx.bK)*dE*ones(N*J) + end + end + dsL1=zeros(Float64,N*J) + if approx.bL1!=0. + dsL1+=approx.bL1*dS + end + if approx.bL1!=1. + dsL1+=(1-approx.bL1)*dE*ones(N*J) + end + + dX=(dsK-dsL1+dII)./sL1-X./sL1.*dsL1 + dY=(dS-dsL1+dG+dJJ/2)./sL1-Y./sL1.*dsL1 + + out=((Y.+1).*dX./(X.+1)-dY)./(2*(X.+1)).*dotPhi((Y.+1)./((X.+1).^2))+(Y.+1)./(2*(X.+1).^3).*(2*(Y.+1)./(X.+1).*dX-dY).*dotdPhi((Y.+1)./(X.+1).^2) + return out +end diff --git a/src/chebyshev.jl b/src/chebyshev.jl new file mode 100644 index 0000000..28c8f1f --- /dev/null +++ b/src/chebyshev.jl @@ -0,0 +1,279 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# Chebyshev expansion +@everywhere function chebyshev(a,taus,weights,P,N,J,nu) + out=zeros(Float64,J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + for j in 1:N + out[zeta*(P+1)+n+1]+=(2-(n==0 ? 1 : 0))/2*weights[2][j]*cos(n*pi*(1+weights[1][j])/2)*a[zeta*N+j]/(1-(taus[zeta+2]-taus[zeta+1])/2*sin(pi*weights[1][j]/2)+(taus[zeta+2]+taus[zeta+1])/2)^nu + end + end + end + + return out +end + +# evaluate function from Chebyshev expansion +@everywhere function chebyshev_eval(Fa,x,taus,chebyshev,P,J,nu) + # change variable + tau=(1-x)/(1+x) + + out=0. + for zeta in 0:J-1 + # check that the spline is right + if tau=taus[zeta+1] + for n in 0:P + out+=Fa[zeta*(P+1)+n+1]*chebyshev[n+1]((2*tau-(taus[zeta+1]+taus[zeta+2]))/(taus[zeta+2]-taus[zeta+1])) + end + break + end + end + + return (1+tau)^nu*out +end + + +# convolution +# input the Chebyshev expansion of a and b, as well as the A matrix +@everywhere function conv_chebyshev(Fa,Fb,A) + out=zeros(Float64,length(A)) + for i in 1:length(A) + out[i]=dot(Fa,A[i]*Fb) + end + return out +end + +# +@everywhere function avg_chebyshev(Fa,Fb,A0) + return dot(Fa,A0*Fb) +end + +# 1_n * a +@everywhere function conv_one_chebyshev(n,zetapp,Fa,A,taus,weights,P,N,J,nu1) + out=zeros(Float64,N*J) + for m in 1:N*J + for l in 0:P + for p in 1:J*(P+1) + out[m]+=(2-(l==0 ? 1 : 0))/2*weights[2][n]*cos(l*pi*(1+weights[1][n])/2)/(1-(taus[zetapp+2]-taus[zetapp+1])/2*sin(pi*weights[1][n]/2)+(taus[zetapp+2]+taus[zetapp+1])/2)^nu1*A[m][zetapp*(P+1)+l+1,p]*Fa[p] + end + end + end + return out +end +# a * v +@everywhere function conv_v_chebyshev(a,Upsilon,k,taus,weights,N,J) + out=zeros(Float64,J*N) + for i in 1:J*N + for zetap in 0:J-1 + for j in 1:N + xj=weights[1][j] + out[i]+=(taus[zetap+2]-taus[zetap+1])/(32*pi)*weights[2][j]*cos(pi*xj/2)*(1+k[zetap*N+j])^2*k[zetap*N+j]*a[zetap*N+j]*Upsilon[zetap*N+j][i] + end + end + end + return out +end +@everywhere function conv_one_v_chebyshev(n,zetap,Upsilon,k,taus,weights,N,J) + out=zeros(Float64,J*N) + xj=weights[1][n] + for i in 1:J*N + out[i]=(taus[zetap+2]-taus[zetap+1])/(32*pi)*weights[2][n]*cos(pi*xj/2)*(1+k[zetap*N+n])^2*k[zetap*N+n]*Upsilon[zetap*N+n][i] + end + return out +end + +# +@everywhere function avg_v_chebyshev(a,Upsilon0,k,taus,weights,N,J) + out=0. + for zetap in 0:J-1 + for j in 1:N + xj=weights[1][j] + out+=(taus[zetap+2]-taus[zetap+1])/(32*pi)*weights[2][j]*cos(pi*xj/2)*(1+k[zetap*N+j])^2*k[zetap*N+j]*a[zetap*N+j]*Upsilon0[zetap*N+j] + end + end + return out +end +# <1_nv> +@everywhere function avg_one_v_chebyshev(n,zetap,Upsilon0,k,taus,weights,N) + xj=weights[1][n] + return (taus[zetap+2]-taus[zetap+1])/(32*pi)*weights[2][n]*cos(pi*xj/2)*(1+k[zetap*N+n])^2*k[zetap*N+n]*Upsilon0[zetap*N+n] +end + +# compute \int dq dxi u1(k-xi)u2(q)u3(xi)u4(k-q)u5(xi-q) +@everywhere function double_conv_S_chebyshev(FU1,FU2,FU3,FU4,FU5,Abar) + out=zeros(Float64,length(Abar)) + for i in 1:length(Abar) + for j1 in 1:length(FU1) + for j2 in 1:length(FU2) + for j3 in 1:length(FU3) + for j4 in 1:length(FU4) + for j5 in 1:length(FU5) + out[i]+=Abar[i][j1,j2,j3,j4,j5]*FU1[j1]*FU2[j2]*FU3[j3]*FU4[j4]*FU5[j5] + end + end + end + end + end + end + return out +end + + +# compute A +@everywhere function Amat(k,weights,taus,T,P,N,J,nua,nub) + out=Array{Array{Float64,2},1}(undef,J*N) + for i in 1:J*N + out[i]=zeros(Float64,J*(P+1),J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[i][zeta*(P+1)+n+1,zetap*(P+1)+m+1]=1/(pi^2*k[i])*integrate_legendre(tau->(1-tau)/(1+tau)^(3-nua)*T[n+1]((2*tau-(taus[zeta+1]+taus[zeta+2]))/(taus[zeta+2]-taus[zeta+1]))*(alpham(k[i],tau)>taus[zetap+2] || alphap(k[i],tau)(1-sigma)/(1+sigma)^(3-nub)*T[m+1]((2*sigma-(taus[zetap+1]+taus[zetap+2]))/(taus[zetap+2]-taus[zetap+1])),max(taus[zetap+1],alpham(k[i],tau)),min(taus[zetap+2],alphap(k[i],tau)),weights)),taus[zeta+1],taus[zeta+2],weights) + end + end + end + end + end + + return out +end + +# compute Upsilon +@everywhere function Upsilonmat(k,v,weights) + out=Array{Array{Float64,1},1}(undef,length(k)) + for i in 1:length(k) + out[i]=Array{Float64,1}(undef,length(k)) + for j in 1:length(k) + out[i][j]=integrate_legendre(s->s*v(s)/k[j],abs(k[j]-k[i]),k[j]+k[i],weights) + end + end + return out +end +@everywhere function Upsilon0mat(k,v,weights) + out=Array{Float64,1}(undef,length(k)) + for j in 1:length(k) + out[j]=2*k[j]*v(k[j]) + end + return out +end + +# alpha_- +@everywhere function alpham(k,t) + return (1-k-(1-t)/(1+t))/(1+k+(1-t)/(1+t)) +end +# alpha_+ +@everywhere function alphap(k,t) + return (1-abs(k-(1-t)/(1+t)))/(1+abs(k-(1-t)/(1+t))) +end + + +# compute \bar A +@everywhere function barAmat(k,weights,taus,T,P,N,J,nu1,nu2,nu3,nu4,nu5) + out=zeros(Float64,J*(P+1),J*(P+1),J*(P+1),J*(P+1),J*(P+1)) + for zeta1 in 0:J-1 + for n1 in 0:P + for zeta2 in 0:J-1 + for n2 in 0:P + for zeta3 in 0:J-1 + for n3 in 0:P + for zeta4 in 0:J-1 + for n4 in 0:P + for zeta5 in 0:J-1 + for n5 in 0:P + out[zeta1*(P+1)+n1+1, + zeta2*(P+1)+n2+1, + zeta3*(P+1)+n3+1, + zeta4*(P+1)+n4+1, + zeta5*(P+1)+n5+1]=1/((2*pi)^5*k^2)*integrate_legendre(tau->barAmat_int1(tau,k,taus,T,weights,nu1,nu2,nu3,nu4,nu5,zeta1,zeta2,zeta3,zeta4,zeta5,n1,n2,n3,n4,n5),taus[zeta1+1],taus[zeta1+2],weights) + end + end + end + end + end + end + end + end + end + end + + return out +end +@everywhere function barAmat_int1(tau,k,taus,T,weights,nu1,nu2,nu3,nu4,nu5,zeta1,zeta2,zeta3,zeta4,zeta5,n1,n2,n3,n4,n5) + if(alpham(k,tau)taus[zeta2+1]) + return 2*(1-tau)/(1+tau)^(3-nu1)*T[n1+1]((2*tau-(taus[zeta1+1]+taus[zeta1+2]))/(taus[zeta1+2]-taus[zeta1+1]))*integrate_legendre(sigma->barAmat_int2(tau,sigma,k,taus,T,weights,nu2,nu3,nu4,nu5,zeta2,zeta3,zeta4,zeta5,n2,n3,n4,n5),max(taus[zeta2+1],alpham(k,tau)),min(taus[zeta2+2],alphap(k,tau)),weights) + else + return 0. + end +end +@everywhere function barAmat_int2(tau,sigma,k,taus,T,weights,nu2,nu3,nu4,nu5,zeta2,zeta3,zeta4,zeta5,n2,n3,n4,n5) + return 2*(1-sigma)/(1+sigma)^(3-nu2)*T[n2+1]((2*sigma-(taus[zeta2+1]+taus[zeta2+2]))/(taus[zeta2+2]-taus[zeta2+1]))*integrate_legendre(taup->barAmat_int3(tau,sigma,taup,k,taus,T,weights,nu3,nu4,nu5,zeta3,zeta4,zeta5,n3,n4,n5),taus[zeta3+1],taus[zeta3+2],weights) +end +@everywhere function barAmat_int3(tau,sigma,taup,k,taus,T,weights,nu3,nu4,nu5,zeta3,zeta4,zeta5,n3,n4,n5) + if(alpham(k,taup)taus[zeta4+1]) + return 2*(1-taup)/(1+taup)^(3-nu3)*T[n3+1]((2*taup-(taus[zeta3+1]+taus[zeta3+2]))/(taus[zeta3+2]-taus[zeta3+1]))*integrate_legendre(sigmap->barAmat_int4(tau,sigma,taup,sigmap,k,taus,T,weights,nu4,nu5,zeta4,zeta5,n4,n5),max(taus[zeta4+1],alpham(k,taup)),min(taus[zeta4+2],alphap(k,taup)),weights) + else + return 0. + end +end +@everywhere function barAmat_int4(tau,sigma,taup,sigmap,k,taus,T,weights,nu4,nu5,zeta4,zeta5,n4,n5) + return 2*(1-sigmap)/(1+sigmap)^(3-nu4)*T[n4+1]((2*sigma-(taus[zeta4+1]+taus[zeta4+2]))/(taus[zeta4+2]-taus[zeta4+1]))*integrate_legendre(theta->barAmat_int5(tau,sigma,taup,sigmap,theta,k,taus,T,weights,nu5,zeta5,n5),0.,2*pi,weights) +end +@everywhere function barAmat_int5(tau,sigma,taup,sigmap,theta,k,taus,T,weights,nu5,zeta5,n5) + R=barAmat_R((1-sigma)/(1+sigma),(1-tau)/(1+tau),(1-sigmap)/(1+sigmap),(1-taup)/(1+taup),theta,k) + if((1-R)/(1+R)taus[zeta5+1]) + return (2/(2+R))^nu5*T[n5+1]((2*(1-R)/(1+R)-(taus[zeta5+1]+taus[zeta5+2]))/(taus[zeta5+2]-taus[zeta5+1])) + else + return 0. + end +end +# R(s,t,s',t,theta,k) +@everywhere function barAmat_R(s,t,sp,tp,theta,k) + return sqrt(k^2*(s^2+t^2+sp^2+tp^2)-k^4-(s^2-t^2)*(sp^2-tp^2)-sqrt((4*k^2*s^2-(k^2+s^2-t^2)^2)*(4*k^2*sp^2-(k^2+sp^2-tp^2)^2))*cos(theta))/(sqrt(2)*k) +end + +# compute Chebyshev polynomials +@everywhere function chebyshev_polynomials(P) + T=Array{Polynomial}(undef,P+1) + T[1]=Polynomial([1]) + T[2]=Polynomial([0,1]) + for n in 1:P-1 + # T_n + T[n+2]=2*T[2]*T[n+1]-T[n] + end + + return T +end + +# compute \int f*u dk/(2*pi)^3 +@everywhere function integrate_f_chebyshev(f,u,k,taus,weights,N,J) + out=0. + for zeta in 0:J-1 + for i in 1:N + out+=(taus[zeta+2]-taus[zeta+1])/(16*pi)*weights[2][i]*cos(pi*weights[1][i]/2)*(1+k[zeta*N+i])^2*k[zeta*N+i]^2*u[zeta*N+i]*f(k[zeta*N+i]) + end + end + return out +end + +@everywhere function inverse_fourier_chebyshev(u,x,k,taus,weights,N,J) + out=0. + for zeta in 0:J-1 + for j in 1:N + out+=(taus[zeta+2]-taus[zeta+1])/(16*pi*x)*weights[2][j]*cos(pi*weights[1][j]/2)*(1+k[zeta*N+j])^2*k[zeta*N+j]*u[zeta*N+j]*sin(k[zeta*N+j]*x) + end + end + return out +end diff --git a/src/easyeq.jl b/src/easyeq.jl new file mode 100644 index 0000000..0bde3ab --- /dev/null +++ b/src/easyeq.jl @@ -0,0 +1,411 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# interpolation +@everywhere mutable struct Easyeq_approx + bK::Float64 + bL::Float64 +end + +# compute energy +function easyeq_energy(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,approx) + # compute gaussian quadrature weights + weights=gausslegendre(order) + + # compute initial guess from previous rho + (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx) + for j in 2:nlrho_init + rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1)) + (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx) + end + + # print energy + @printf("% .15e % .15e\n",real(E),err) +end + +# compute energy as a function of rho +function easyeq_energy_rho(rhos,order,a0,v,maxiter,tolerance,approx) + # compute gaussian quadrature weights + weights=gausslegendre(order) + # init u + u=easyeq_init_u(a0,order,weights) + + for j in 1:length(rhos) + # compute u (init newton with previously computed u) + (u,E,err)=easyeq_hatu(u,order,rhos[j],v,maxiter,tolerance,weights,approx) + + @printf("% .15e % .15e % .15e\n",rhos[j],real(E),err) + + end +end + +# compute u(k) +function easyeq_uk(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,approx) + weights=gausslegendre(order) + + # compute initial guess from previous rho + (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx) + for j in 2:nlrho_init + rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1)) + (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx) + end + + for i in 1:order + k=(1-weights[1][i])/(1+weights[1][i]) + @printf("% .15e % .15e\n",k,real(u[i])) + end +end + +# compute u(x) +function easyeq_ux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx) + weights=gausslegendre(order) + + # compute initial guess from previous rho + (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx) + for j in 2:nlrho_init + rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1)) + (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx) + end + + for i in 1:nx + x=xmin+(xmax-xmin)*i/nx + @printf("% .15e % .15e\n",x,real(easyeq_u_x(x,u,weights))) + end +end + +# compute 2u(x)-rho u*u(x) +function easyeq_uux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,approx) + weights=gausslegendre(order) + + # compute initial guess from previous rho + (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx) + for j in 2:nlrho_init + rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1)) + (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx) + end + + for i in 1:nx + x=xmin+(xmax-xmin)*i/nx + @printf("% .15e % .15e\n",x,real(easyeq_u_x(x,2*u-rho*u.*u,weights))) + end +end + +# condensate fraction +function easyeq_condensate_fraction(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,approx) + # compute gaussian quadrature weights + weights=gausslegendre(order) + + # compute initial guess from previous rho + (u,E,err)=easyeq_hatu(easyeq_init_u(a0,order,weights),order,(10.)^minlrho_init,v,maxiter,tolerance,weights,approx) + for j in 2:nlrho_init + rho_tmp=10^(minlrho_init+(log10(rho)-minlrho_init)*(j-1)/(nlrho_init-1)) + (u,E,err)=easyeq_hatu(u,order,rho_tmp,v,maxiter,tolerance,weights,approx) + end + + # compute eta + eta=easyeq_eta(u,order,rho,v,maxiter,tolerance,weights,approx) + + # print energy + @printf("% .15e % .15e\n",eta,err) +end + +# condensate fraction as a function of rho +function easyeq_condensate_fraction_rho(rhos,order,a0,v,maxiter,tolerance,approx) + weights=gausslegendre(order) + # init u + u=easyeq_init_u(a0,order,weights) + + for j in 1:length(rhos) + # compute u (init newton with previously computed u) + (u,E,err)=easyeq_hatu(u,order,rhos[j],v,maxiter,tolerance,weights,approx) + + # compute eta + eta=easyeq_eta(u,order,rhos[j],v,maxiter,tolerance,weights,approx) + + @printf("% .15e % .15e % .15e\n",rhos[j],eta,err) + end +end + + +# initialize u +@everywhere function easyeq_init_u(a0,order,weights) + u=zeros(Float64,order) + for j in 1:order + # transformed k + k=(1-weights[1][j])/(1+weights[1][j]) + u[j]=4*pi*a0/k^2 + end + + return u +end + +# \hat u(k) computed using Newton +@everywhere function easyeq_hatu(u0,order,rho,v,maxiter,tolerance,weights,approx) + # initialize V and Eta + (V,V0)=easyeq_init_v(weights,v) + (Eta,Eta0)=easyeq_init_H(weights,v) + + # init u + u=rho*u0 + + # iterate + err=Inf + for i in 1:maxiter-1 + new=u-inv(easyeq_dXi(u,V,V0,Eta,Eta0,weights,rho,approx))*easyeq_Xi(u,V,V0,Eta,Eta0,weights,rho,approx) + + err=norm(new-u)/norm(u) + if(errt ? 2*t/x : 2)* integrate_legendre(y->2*pi*((x+t)*y+abs(x-t)*(1-y))*v((x+t)*y+abs(x-t)*(1-y)),0,1,weights) +end + +# initialize V +@everywhere function easyeq_init_v(weights,v) + order=length(weights[1]) + V=Array{Float64}(undef,order) + V0=v(0) + for i in 1:order + k=(1-weights[1][i])/(1+weights[1][i]) + V[i]=v(k) + end + return(V,V0) +end + +# initialize Eta +@everywhere function easyeq_init_H(weights,v) + order=length(weights[1]) + Eta=Array{Array{Float64}}(undef,order) + Eta0=Array{Float64}(undef,order) + for i in 1:order + k=(1-weights[1][i])/(1+weights[1][i]) + Eta[i]=Array{Float64}(undef,order) + for j in 1:order + y=(weights[1][j]+1)/2 + Eta[i][j]=easyeq_H(k,(1-y)/y,weights,v) + end + y=(weights[1][i]+1)/2 + Eta0[i]=easyeq_H(0,(1-y)/y,weights,v) + end + return(Eta,Eta0) +end + +# Xi(u) +@everywhere function easyeq_Xi(u,V,V0,Eta,Eta0,weights,rho,approx) + order=length(weights[1]) + + # init + out=zeros(Float64,order) + + # compute E before running the loop + E=easyeq_en(u,V0,Eta0,rho,weights) + + for i in 1:order + # k_i + k=(1-weights[1][i])/(1+weights[1][i]) + # S_i + S=V[i]-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta[i].*u,0,1,weights) + + # A_K,i + A=0. + if approx.bK!=0. + A+=approx.bK*S + end + if approx.bK!=1. + A+=(1-approx.bK)*E + end + + # T + if approx.bK==1. + T=1. + else + T=S/A + end + + # B + if approx.bK==approx.bL + B=1. + else + B=(approx.bL*S+(1-approx.bL*E))/(approx.bK*S+(1-approx.bK*E)) + end + + # X_i + X=k^2/(2*A*rho) + + # U_i + out[i]=u[i]-T/(2*(X+1))*Phi(B*T/(X+1)^2) + end + + return out +end + +# derivative of Xi +@everywhere function easyeq_dXi(u,V,V0,Eta,Eta0,weights,rho,approx) + order=length(weights[1]) + + # init + out=zeros(Float64,order,order) + + # compute E before the loop + E=easyeq_en(u,V0,Eta0,rho,weights) + + for i in 1:order + # k_i + k=(1-weights[1][i])/(1+weights[1][i]) + # S_i + S=V[i]-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta[i].*u,0,1,weights) + + # A_K,i + A=0. + if approx.bK!=0. + A+=approx.bK*S + end + if approx.bK!=1. + A+=(1-approx.bK)*E + end + + # T + if approx.bK==1. + T=1. + else + T=S/A + end + + # B + if approx.bK==approx.bL + B=1. + else + B=(approx.bL*S+(1-approx.bL*E))/(approx.bK*S+(1-approx.bK*E)) + end + + # X_i + X=k^2/(2*A*rho) + + for j in 1:order + y=(weights[1][j]+1)/2 + dS=-1/rho*(1-y)*Eta[i][j]/(2*(2*pi)^3*y^3)*weights[2][j] + dE=-1/rho*(1-y)*Eta0[j]/(2*(2*pi)^3*y^3)*weights[2][j] + + # dA + dA=0. + if approx.bK!=0. + dA+=approx.bK*dS + end + if approx.bK!=1. + dA+=(1-approx.bK)*dE + end + + # dT + if approx.bK==1. + dT=0. + else + dT=(1-approx.bK)*(E*dS-S*dE)/A^2 + end + + # dB + if approx.bK==approx.bL + dB=0. + else + dB=(approx.bL*(1-approx.bK)-approx.bK*(1-approx.bL))*(E*dS-S*dE)/(approx.bK*S+(1-approx.bK*E))^2 + end + + dX=-k^2/(2*A^2*rho)*dA + + out[i,j]=(i==j ? 1 : 0)-(dT-T*dX/(X+1))/(2*(X+1))*Phi(B*T/(X+1)^2)-T/(2*(X+1)^3)*(B*dT+T*dB-2*B*T*dX/(X+1))*dPhi(B*T/(X+1)^2) + end + end + + return out +end + +# derivative of Xi with respect to mu +@everywhere function easyeq_dXidmu(u,V,V0,Eta,Eta0,weights,rho,approx) + order=length(weights[1]) + + # init + out=zeros(Float64,order) + + # compute E before running the loop + E=easyeq_en(u,V0,Eta0,rho,weights) + + for i in 1:order + # k_i + k=(1-weights[1][i])/(1+weights[1][i]) + # S_i + S=V[i]-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta[i].*u,0,1,weights) + + # A_K,i + A=0. + if approx.bK!=0. + A+=approx.bK*S + end + if approx.bK!=1. + A+=(1-approx.bK)*E + end + + # T + if approx.bK==1. + T=1. + else + T=S/A + end + + # B + if approx.bK==approx.bL + B=1. + else + B=(approx.bL*S+(1-approx.bL*E))/(approx.bK*S+(1-approx.bK*E)) + end + + # X_i + X=k^2/(2*A*rho) + + out[i]=T/(2*rho*A*(X+1)^2)*Phi(B*T/(X+1)^2)+B*T^2/(rho*A*(X+1)^4)*dPhi(B*T/(X+1)^2) + end + + return out +end + +# energy +@everywhere function easyeq_en(u,V0,Eta0,rho,weights) + return V0-1/(rho*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta0.*u,0,1,weights) +end + +# condensate fraction +@everywhere function easyeq_eta(u,order,rho,v,maxiter,tolerance,weights,approx) + (V,V0)=easyeq_init_v(weights,v) + (Eta,Eta0)=easyeq_init_H(weights,v) + + du=-inv(easyeq_dXi(rho*u,V,V0,Eta,Eta0,weights,rho,approx))*easyeq_dXidmu(rho*u,V,V0,Eta,Eta0,weights,rho,approx) + + eta=-1/(2*(2*pi)^3)*integrate_legendre_sampled(y->(1-y)/y^3,Eta0.*du,0,1,weights) + + return eta +end + +# inverse Fourier transform +@everywhere function easyeq_u_x(x,u,weights) + order=length(weights[1]) + out=integrate_legendre_sampled(y->(1-y)/y^3*sin(x*(1-y)/y)/x/(2*pi^2),u,0,1,weights) + return out +end diff --git a/src/integration.jl b/src/integration.jl new file mode 100644 index 0000000..9be4641 --- /dev/null +++ b/src/integration.jl @@ -0,0 +1,58 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# approximate \int_a^b f using Gauss-Legendre quadratures +@everywhere function integrate_legendre(f,a,b,weights) + out=0 + for i in 1:length(weights[1]) + out+=(b-a)/2*weights[2][i]*f((b-a)/2*weights[1][i]+(b+a)/2) + end + return out +end +# \int f*g where g is sampled at the Legendre nodes +@everywhere function integrate_legendre_sampled(f,g,a,b,weights) + out=0 + for i in 1:length(weights[1]) + out+=(b-a)/2*weights[2][i]*f((b-a)/2*weights[1][i]+(b+a)/2)*g[i] + end + return out +end + + +# approximate \int_a^b f/sqrt((b-x)(x-a)) using Gauss-Chebyshev quadratures +@everywhere function integrate_chebyshev(f,a,b,N) + out=0 + for i in 1:N + out=out+pi/N*f((b-a)/2*cos((2*i-1)/(2*N)*pi)+(b+a)/2) + end + return out +end + +# approximate \int_0^\infty dr f(r)*exp(-a*r) using Gauss-Chebyshev quadratures +@everywhere function integrate_laguerre(f,a,weights_gL) + out=0. + for i in 1:length(weights_gL[1]) + out+=1/a*f(weights_gL[1][i]/a)*weights_gL[2][i] + end + return out +end + +# Hann window +@everywhere function hann(x,L) + if abs(x)x[length(x)] + return y[length(y)] + elseif x0x[i+1] + return bracket(x0,x,i+1,b) + else + return i + end +end + + +# polynomial interpolation of a family of points +@everywhere function poly_interpolation(x,y) + # init for recursion + rec=Array{Polynomial{Float64}}(undef,length(x)) + for i in 1:length(x) + rec[i]=Polynomial([1.]) + end + + # compute \prod (x-x_i) + poly_interpolation_rec(rec,x,1,length(x)) + + # sum terms together + out=0. + for i in 1:length(y) + out+=rec[i]/rec[i](x[i])*y[i] + end + + return out +end +# recursive helper function +@everywhere function poly_interpolation_rec(out,x,a,b) + if a==b + return + end + # mid point + mid=floor(Int64,(a+b)/2) + # multiply left and right of mid + right=Polynomial([1.]) + for i in mid+1:b + right*=Polynomial([-x[i],1.]) + end + left=Polynomial([1.]) + for i in a:mid + left*=Polynomial([-x[i],1.]) + end + + # multiply into left and right + for i in a:mid + out[i]*=right + end + for i in mid+1:b + out[i]*=left + end + + # recurse + poly_interpolation_rec(out,x,a,mid) + poly_interpolation_rec(out,x,mid+1,b) + + return +end +## the following does the same, but has complexity N^2, the function above has N*log(N) +#@everywhere function poly_interpolation(x,y) +# out=Polynomial([0.]) +# for i in 1:length(x) +# prod=Polynomial([1.]) +# for j in 1:length(x) +# if j!=i +# prod*=Polynomial([-x[j]/(x[i]-x[j]),1/(x[i]-x[j])]) +# end +# end +# out+=prod*y[i] +# end +# +# return out +#end + diff --git a/src/main.jl b/src/main.jl new file mode 100644 index 0000000..382fe6b --- /dev/null +++ b/src/main.jl @@ -0,0 +1,443 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +using Distributed +@everywhere using FastGaussQuadrature +@everywhere using LinearAlgebra +@everywhere using Polynomials +@everywhere using Printf +@everywhere using SpecialFunctions +include("chebyshev.jl") +include("integration.jl") +include("interpolation.jl") +include("tools.jl") +include("potentials.jl") +include("print.jl") +include("easyeq.jl") +include("simpleq-Kv.jl") +include("simpleq-iteration.jl") +include("simpleq-hardcore.jl") +include("anyeq.jl") +function main() + ## defaults + + # values of rho,e, when needed + rho=1e-6 + e=1e-4 + + # incrementally initialize Newton algorithm + nlrho_init=1 + + # potential + v=k->v_exp(k,1.) + a0=a0_exp(1.) + # arguments of the potential + v_param_a=1. + v_param_b=1. + v_param_c=1. + v_param_e=1. + + # plot range when plotting in rho + minlrho=-6 + maxlrho=2 + nlrho=100 + rhos=Array{Float64}(undef,0) + # plot range when plotting in e + minle=-6 + maxle=2 + nle=100 + es=Array{Float64}(undef,0) + # plot range when plotting in x + xmin=0 + xmax=100 + nx=100 + + # cutoffs + tolerance=1e-11 + order=100 + maxiter=21 + + # for anyeq + # P + P=11 + # N + N=12 + # number of splines + J=10 + + # starting rho from which to incrementally initialize Newton algorithm + # default must be set after reading rho, if not set explicitly + minlrho_init=nothing + + # Hann window for Fourier transforms + windowL=1e3 + + # approximation for easyeq + # bK,bL + easyeq_simpleq_approx=Easyeq_approx(0.,0.) + easyeq_medeq_approx=Easyeq_approx(1.,1.) + easyeq_approx=easyeq_simpleq_approx + + # approximation for anyeq + # aK,bK,gK,aL1,bL1,aL2,bL2,gL2,aL3,bL3,gl3 + anyeq_simpleq_approx=Anyeq_approx(0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.) + anyeq_bigeq_approx=Anyeq_approx(1.,1.,1.,1.,1.,1.,1.,1.,0.,0.,0.) + anyeq_fulleq_approx=Anyeq_approx(1.,1.,1.,1.,1.,1.,1.,1.,1.,0.,1.) + anyeq_medeq_approx=Anyeq_approx(0.,1.,1.,0.,1.,0.,0.,0.,0.,0.,0.) + anyeq_compleq_approx=Anyeq_approx(1.,1.,1.,1.,1.,1.,1.,1.,1.,1.,1.) + anyeq_approx=anyeq_bigeq_approx + + # read cli arguments + (params,potential,method,savefile,command)=read_args(ARGS) + + # read params + if params!="" + for param in split(params,";") + terms=split(param,"=") + if length(terms)!=2 + print(stderr,"error: could not read parameter '",param,"'.\n") + exit(-1) + end + lhs=terms[1] + rhs=terms[2] + if lhs=="rho" + rho=parse(Float64,rhs) + elseif lhs=="minlrho_init" + minlrho_init=parse(Float64,rhs) + elseif lhs=="nlrho_init" + nlrho_init=parse(Int64,rhs) + elseif lhs=="e" + e=parse(Float64,rhs) + elseif lhs=="tolerance" + tolerance=parse(Float64,rhs) + elseif lhs=="order" + order=parse(Int64,rhs) + elseif lhs=="maxiter" + maxiter=parse(Int64,rhs) + elseif lhs=="rhos" + rhos=parse_list(rhs) + elseif lhs=="minlrho" + minlrho=parse(Float64,rhs) + elseif lhs=="maxlrho" + maxlrho=parse(Float64,rhs) + elseif lhs=="nlrho" + nlrho=parse(Int64,rhs) + elseif lhs=="es" + es=parse_list(rhs) + elseif lhs=="minle" + minle=parse(Float64,rhs) + elseif lhs=="maxle" + maxle=parse(Float64,rhs) + elseif lhs=="nle" + nle=parse(Int64,rhs) + elseif lhs=="xmin" + xmin=parse(Float64,rhs) + elseif lhs=="xmax" + xmax=parse(Float64,rhs) + elseif lhs=="nx" + nx=parse(Int64,rhs) + elseif lhs=="P" + P=parse(Int64,rhs) + elseif lhs=="N" + N=parse(Int64,rhs) + elseif lhs=="J" + J=parse(Int64,rhs) + elseif lhs=="window_L" + windowL=parse(Float64,rhs) + elseif lhs=="aK" + anyeq_approx.aK=parse(Float64,rhs) + elseif lhs=="bK" + anyeq_approx.bK=parse(Float64,rhs) + easyeq_approx.bK=parse(Float64,rhs) + elseif lhs=="gK" + anyeq_approx.gK=parse(Float64,rhs) + elseif lhs=="aL1" + anyeq_approx.aL1=parse(Float64,rhs) + elseif lhs=="bL" + easyeq_approx.bL=parse(Float64,rhs) + elseif lhs=="bL1" + anyeq_approx.bL1=parse(Float64,rhs) + elseif lhs=="aL2" + anyeq_approx.aL2=parse(Float64,rhs) + elseif lhs=="bL2" + anyeq_approx.bL2=parse(Float64,rhs) + elseif lhs=="gL2" + anyeq_approx.gL2=parse(Float64,rhs) + elseif lhs=="aL3" + anyeq_approx.aL3=parse(Float64,rhs) + elseif lhs=="bL3" + anyeq_approx.bL3=parse(Float64,rhs) + elseif lhs=="gL3" + anyeq_approx.gL3=parse(Float64,rhs) + elseif lhs=="v_a" + v_param_a=parse(Float64,rhs) + elseif lhs=="v_b" + v_param_b=parse(Float64,rhs) + elseif lhs=="v_c" + v_param_c=parse(Float64,rhs) + elseif lhs=="v_e" + v_param_e=parse(Float64,rhs) + elseif lhs=="eq" + if rhs=="simpleq" + easyeq_approx=easyeq_simpleq_approx + anyeq_approx=anyeq_simpleq_approx + elseif rhs=="medeq" + easyeq_approx=easyeq_medeq_approx + anyeq_approx=anyeq_medeq_approx + elseif rhs=="bigeq" + anyeq_approx=anyeq_bigeq_approx + elseif rhs=="fulleq" + anyeq_approx=anyeq_fulleq_approx + elseif rhs=="compleq" + anyeq_approx=anyeq_compleq_approx + else + print(stderr,"error: unrecognized equation: ",rhs,"\n") + exit(-1) + end + else + print(stderr,"unrecognized parameter '",lhs,"'.\n") + exit(-1) + end + end + end + + ## read potential + if potential=="exp" + v=k->v_exp(k,v_param_a) + a0=a0_exp(v_param_a) + elseif potential=="expcry" + v=k->v_expcry(k,v_param_a,v_param_b) + a0=a0_expcry(v_param_a,v_param_b) + elseif potential=="npt" + v=k->v_npt(k) + a0=a0_npt() + elseif potential=="alg" + v=v_alg + a0=a0_alg + elseif potential=="algwell" + v=v_algwell + a0=a0_algwell + elseif potential=="exact" + v=k->v_exact(k,v_param_b,v_param_c,v_param_e) + a0=a0_exact(v_param_b,v_param_c,v_param_e) + elseif potential=="tent" + v=k->v_tent(k,v_param_a,v_param_b) + a0=a0_tent(v_param_a,v_param_b) + else + print(stderr,"unrecognized potential '",potential,"'.\n'") + exit(-1) + end + + ## set parameters + # rhos + if length(rhos)==0 + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? 10^minlrho : 10^(minlrho+(maxlrho-minlrho)/(nlrho-1)*j)) + end + end + # es + if length(es)==0 + es=Array{Float64}(undef,nle) + for j in 0:nle-1 + es[j+1]=(nle==1 ? 10^minle : 10^(minle+(maxle-minle)/(nle-1)*j)) + end + end + + # default minlrho_init + if (minlrho_init==nothing) + minlrho_init=log10(rho) + end + + # splines + taus=Array{Float64}(undef,J+1) + for j in 0:J + taus[j+1]=-1+2*j/J + end + + ## run command + if method=="easyeq" + if command=="energy" + easyeq_energy(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,easyeq_approx) + # e(rho) + elseif command=="energy_rho" + easyeq_energy_rho(rhos,order,a0,v,maxiter,tolerance,easyeq_approx) + # u(k) + elseif command=="uk" + easyeq_uk(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,easyeq_approx) + # u(x) + elseif command=="ux" + easyeq_ux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,easyeq_approx) + elseif command=="uux" + easyeq_uux(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,easyeq_approx) + # condensate fraction + elseif command=="condensate_fraction" + easyeq_condensate_fraction(minlrho_init,nlrho_init,order,rho,a0,v,maxiter,tolerance,easyeq_approx) + elseif command=="condensate_fraction_rho" + easyeq_condensate_fraction_rho(rhos,order,a0,v,maxiter,tolerance,easyeq_approx) + else + print(stderr,"unrecognized command '",command,"'.\n") + exit(-1) + end + elseif method=="simpleq-hardcore" + if command=="energy_rho" + simpleq_hardcore_energy_rho(rhos,taus,P,N,J,maxiter,tolerance) + elseif command=="ux" + simpleq_hardcore_ux(rho,taus,P,N,J,maxiter,tolerance) + elseif command=="condensate_fraction_rho" + simpleq_hardcore_condensate_fraction_rho(rhos,taus,P,N,J,maxiter,tolerance) + end + elseif method=="simpleq-iteration" + # u_n(x) using iteration + if command=="u" + simpleq_iteration_ux(order,e,v,maxiter,xmin,xmax,nx) + # rho(e) using iteration + elseif command=="rho_e" + simpleq_iteration_rho_e(es,order,v,maxiter) + else + print(stderr,"unrecognized command '",command,"'.\n") + exit(-1) + end + elseif method=="anyeq" + if command=="save_Abar" + anyeq_save_Abar(taus,P,N,J,v,anyeq_approx) + elseif command=="energy" + anyeq_energy(rho,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile) + # e(rho) + elseif command=="energy_rho" + anyeq_energy_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile) + elseif command=="energy_rho_init_prevrho" + anyeq_energy_rho_init_prevrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile) + elseif command=="energy_rho_init_nextrho" + anyeq_energy_rho_init_nextrho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile) + # u(j) + elseif command=="uk" + anyeq_uk(minlrho_init,nlrho_init,taus,P,N,J,rho,a0,v,maxiter,tolerance,anyeq_approx,savefile) + # u(j) + elseif command=="ux" + anyeq_ux(minlrho_init,nlrho_init,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,anyeq_approx,savefile) + # condensate fraction + elseif command=="condensate_fraction" + anyeq_condensate_fraction(rho,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile) + elseif command=="condensate_fraction_rho" + anyeq_condensate_fraction_rho(rhos,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile) + # momentum distribution + elseif command=="momentum_distribution" + anyeq_momentum_distribution(rho,minlrho_init,nlrho_init,taus,P,N,J,a0,v,maxiter,tolerance,anyeq_approx,savefile) + elseif command=="2pt" + anyeq_2pt_correlation(minlrho_init,nlrho_init,taus,P,N,J,windowL,rho,a0,v,maxiter,tolerance,xmin,xmax,nx,anyeq_approx,savefile) + else + print(stderr,"unrecognized command: '",command,"'\n") + exit(-1) + end + elseif method=="simpleq-Kv" + if command=="2pt" + simpleq_Kv_2pt(minlrho_init,nlrho_init,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx) + elseif command=="condensate_fraction_rho" + simpleq_Kv_condensate_fraction(rhos,taus,P,N,J,a0,v,maxiter,tolerance) + elseif command=="Kv" + simpleq_Kv_Kv(minlrho_init,nlrho_init,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx) + else + print(stderr,"unrecognized command: '",command,"'\n") + exit(-1) + end + else + print(stderr,"unrecognized method: '",method,"'\n") + exit(-1) + end + +end + +# parse a comma separated list as an array of Float64 +function parse_list(str) + elems=split(str,",") + out=Array{Float64}(undef,length(elems)) + for i in 1:length(elems) + out[i]=parse(Float64,elems[i]) + end + return out +end + +# read cli arguments +function read_args(ARGS) + # flag + flag="" + + # output strings + params="" + # default potential + potential="exp" + # default method + method="easyeq" + + savefile="" + command="" + + # loop over arguments + for arg in ARGS + # argument that starts with a dash + if arg[1]=='-' + # go through characters after dash + for char in arg[2:length(arg)] + + # set params + if char=='p' + # raise flag + flag="params" + elseif char=='U' + # raise flag + flag="potential" + elseif char=='M' + # raise flag + flag="method" + elseif char=='s' + # raise flag + flag="savefile" + else + print_usage() + exit(-1) + end + end + # arguments that do not start with a dash + else + if flag=="params" + params=arg + elseif flag=="potential" + potential=arg + elseif flag=="method" + method=arg + elseif flag=="savefile" + savefile=arg + else + command=arg + end + # reset flag + flag="" + end + end + + if command=="" + print_usage() + exit(-1) + end + + return (params,potential,method,savefile,command) +end + +# usage +function print_usage() + print(stderr,"usage: simplesolv [-p params] [-U potential] [-M method] [-s savefile] \n") +end + +main() diff --git a/src/potentials.jl b/src/potentials.jl new file mode 100644 index 0000000..46cafc0 --- /dev/null +++ b/src/potentials.jl @@ -0,0 +1,84 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# exponential potential in 3 dimensions +@everywhere function v_exp(k,a) + return 8*pi/(1+k^2)^2*a +end +@everywhere function a0_exp(a) + if a>0. + return log(a)+2*MathConstants.eulergamma+2*besselk(0,2*sqrt(a))/besseli(0,2*sqrt(a)) + elseif a<0. + return log(-a)+2*MathConstants.eulergamma-pi*bessely(0,2*sqrt(-a))/besselj(0,2*sqrt(-a)) + else + return 0. + end +end + +# exp(-x)-a*exp(-b*x) in 3 dimensions +@everywhere function v_expcry(k,a,b) + return 8*pi*((1+k^2)^(-2)-a*b*(b^2+k^2)^(-2)) +end +@everywhere function a0_expcry(a,b) + return 1.21751642717932720441274114683413710125487579284827462 #ish +end + +# x^2*exp(-|x|) in 3 dimensions +@everywhere function v_npt(k) + return 96*pi*(1-k^2)/(1+k^2)^4 +end +@everywhere function a0_npt() + return 1. #ish +end + +# 1/(1+x^4/4) potential in 3 dimensions +@everywhere function v_alg(k) + if(k==0) + return 4*pi^2 + else + return 4*pi^2*exp(-k)*sin(k)/k + end +end +a0_alg=1. #ish + +# (1+a x^4)/(1+x^2)^4 potential in 3 dimensions +@everywhere function v_algwell(k) + a=4 + return pi^2/24*exp(-k)*(a*(k^2-9*k+15)+k^2+3*k+3) +end +a0_algwell=1. #ish + +# potential corresponding to the exact solution c/(1+b^2x^2)^2 +@everywhere function v_exact(k,b,c,e) + if k!=0 + return 48*pi^2*((18+3*sqrt(c)-(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3+sqrt(c))^2*sqrt(c))*exp(-sqrt(1-sqrt(c))*k/b)+(-18+3*sqrt(c)+(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3-sqrt(c))^2*sqrt(c))*exp(-sqrt(1+sqrt(c))*k/b)+(1-k/b)/2*exp(-k/b)-c*e/b^2*(3*(9-c)*k/b+8*c)/(8*(9-c)^2)*exp(-2*k/b))/k + else + return 48*pi^2*(-sqrt(1-sqrt(c))/b*(18+3*sqrt(c)-(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3+sqrt(c))^2*sqrt(c))-sqrt(1+sqrt(c))/b*(-18+3*sqrt(c)+(4-3*e/b^2)*c-(1-2*e/b^2)*c^1.5)/(4*(3-sqrt(c))^2*sqrt(c))-1/b-c*e/b^2*(27-19*c)/(8*(9-c)^2)) + end +end +@everywhere function a0_exact(b,c,e) + return 1. #ish +end + +# tent potential (convolution of soft sphere with itself): a*pi/12*(2*|x|/b-2)^2*(2*|x|/b+4) for |x|1 + @printf(stderr,"\r") + end + @printf(stderr,"%d/%d",j,tot) + end + if j==tot + @printf(stderr,"\r") + @printf(stderr,"%d/%d\n",j,tot) + end +end + +# print progress of two indices at once +@everywhere function progress_mat( + j1::Int64, + tot1::Int64, + j2::Int64, + tot2::Int64, + freq::Int64 +) + if ((j1-1)*tot2+j2-1)%ceil(Int,tot1*tot2/freq)==0 + if j1>1 || j2>1 + @printf(stderr,"\r") + end + @printf(stderr,"%2d/%2d, %2d/%2d",j1,tot1,j2,tot2) + end + if j1==tot1 && j2==tot2 + @printf(stderr,"\r") + @printf(stderr,"%2d/%2d, %2d/%2d\n",j1,tot1,j2,tot2) + end +end + diff --git a/src/simpleq-Kv.jl b/src/simpleq-Kv.jl new file mode 100644 index 0000000..8789656 --- /dev/null +++ b/src/simpleq-Kv.jl @@ -0,0 +1,119 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# Compute Kv=(-\Delta+v+4e(1-\rho u*))^{-1}v +function anyeq_Kv(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx) + # init vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,Anyeq_approx(0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.)) + + # Kv in Fourier space + Kvk=simpleq_Kv_Kvk(u,V,E,rho,Upsilon,k,taus,weights,N,J) + + # switch to real space + for i in 1:nx + x=xmin+(xmax-xmin)*i/nx + Kv=0. + for zeta in 0:J-1 + for j in 1:N + Kv+=(taus[zeta+2]-taus[zeta+1])/(16*pi*x)*weights[2][j]*cos(pi*weights[1][j]/2)*(1+k[zeta*N+j])^2*k[zeta*N+j]*Kvk[zeta*N+j]*sin(k[zeta*N+j]*x) + end + end + @printf("% .15e % .15e\n",x,Kv) + end +end + +# Compute the condensate fraction for simpleq using Kv +function simpleq_Kv_condensate_fraction(rhos,taus,P,N,J,a0,v,maxiter,tolerance) + # init vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + + # compute initial guess from medeq + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,0.,maxiter,tolerance) + + for j in 1:length(rhos) + (u,E,error)=anyeq_hatu(u0s[j],0.,P,N,J,rhos[j],a0,weights,k,taus,V,V0,A,Abar,Upsilon,Upsilon0,v,maxiter,tolerance,Anyeq_approx(0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.)) + + # Kv in Fourier space + Kvk=simpleq_Kv_Kvk(u,V,E,rho,Upsilon,k,taus,weights,N,J) + + # denominator: (1-rho*\int (Kv)(2u-rho u*u)) + denom=1-rhos[j]*integrate_f_chebyshev(s->1.,Kvk.*(2*u-rhos[j]*u.^2),k,taus,weights,N,J) + + eta=rhos[j]*integrate_f_chebyshev(s->1.,Kvk.*u,k,taus,weights,N,J)/denom + + @printf("% .15e % .15e\n",rhos[j],eta) + end +end + +# Compute the two-point correlation function for simpleq using Kv +function simpleq_Kv_2pt(minlrho,nlrho,taus,P,N,J,rho,a0,v,maxiter,tolerance,xmin,xmax,nx) + # init vectors + (weights,T,k,V,V0,A,Upsilon,Upsilon0)=anyeq_init(taus,P,N,J,v) + + # compute initial guess from medeq + rhos=Array{Float64}(undef,nlrho) + for j in 0:nlrho-1 + rhos[j+1]=(nlrho==1 ? rho : 10^(minlrho+(log10(rho)-minlrho)/(nlrho-1)*j)) + end + u0s=anyeq_init_medeq(rhos,N,J,k,a0,v,maxiter,tolerance) + u0=u0s[nlrho] + + (u,E,error)=anyeq_hatu(u0,P,N,J,rho,a0,weights,k,taus,V,V0,A,nothing,Upsilon,Upsilon0,v,maxiter,tolerance,Anyeq_approx(0.,0.,1.,0.,0.,0.,0.,0.,0.,0.,0.)) + + # Kv in Fourier space + Kvk=simpleq_Kv_Kvk(u,V,E,rho,Upsilon,k,taus,weights,N,J) + + # denominator: (1-rho*\int (Kv)(2u-rho u*u)) + denom=1-rho*integrate_f_chebyshev(s->1.,Kvk.*(2*u-rho*u.^2),k,taus,weights,N,J) + + # Kv, u, u*Kv, u*u*Kv in real space + for i in 1:nx + x=xmin+(xmax-xmin)*i/nx + ux=inverse_fourier_chebyshev(u,x,k,taus,weights,N,J) + Kv=inverse_fourier_chebyshev(Kvk,x,k,taus,weights,N,J) + uKv=inverse_fourier_chebyshev(u.*Kvk,x,k,taus,weights,N,J) + uuKv=inverse_fourier_chebyshev(u.*u.*Kvk,x,k,taus,weights,N,J) + + # correlation in real space + Cx=rho^2*((1-ux)-((1-ux)*Kv-2*rho*uKv+rho^2*uuKv)/denom) + + @printf("% .15e % .15e\n",x,Cx) + end +end + +# Kv +function simpleq_Kv_Kvk(u,V,E,rho,Upsilon,k,taus,weights,N,J) + # (-Delta+v+4e(1-\rho u*)) in Fourier space + M=Array{Float64,2}(undef,N*J,N*J) + for zetapp in 0:J-1 + for n in 1:N + M[:,zetapp*N+n]=conv_one_v_chebyshev(n,zetapp,Upsilon,k,taus,weights,N,J) + end + end + for i in 1:J*N + M[i,i]+=k[i]^2+4*E*(1-rho*u[i]) + end + + return inv(M)*V +end diff --git a/src/simpleq-hardcore.jl b/src/simpleq-hardcore.jl new file mode 100644 index 0000000..ca64f78 --- /dev/null +++ b/src/simpleq-hardcore.jl @@ -0,0 +1,573 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# compute energy as a function of rho +function simpleq_hardcore_energy_rho(rhos,taus,P,N,J,maxiter,tolerance) + ## spawn workers + # number of workers + nw=nworkers() + # split jobs among workers + work=Array{Array{Int64,1},1}(undef,nw) + # init empty arrays + for p in 1:nw + work[p]=zeros(0) + end + for j in 1:length(rhos) + append!(work[j%nw+1],j) + end + + # initialize vectors + (weights,weights_gL,r,T)=simpleq_hardcore_init(taus,P,N,J) + + # initial guess + u0s=Array{Array{Float64}}(undef,length(rhos)) + e0s=Array{Float64}(undef,length(rhos)) + for j in 1:length(rhos) + u0s[j]=Array{Float64}(undef,N*J) + for i in 1:N*J + u0s[j][i]=1/(1+r[i]^2)^2 + end + e0s[j]=pi*rhos[j] + end + + + # save result from each task + us=Array{Array{Float64}}(undef,length(rhos)) + es=Array{Float64}(undef,length(rhos)) + err=Array{Float64}(undef,length(rhos)) + + count=0 + # for each worker + @sync for p in 1:nw + # for each task + @async for j in work[p] + count=count+1 + if count>=nw + progress(count,length(rhos),10000) + end + # run the task + (us[j],es[j],err[j])=remotecall_fetch(simpleq_hardcore_hatu,workers()[p],u0s[j],e0s[j],rhos[j],r,taus,T,weights,weights_gL,P,N,J,maxiter,tolerance) + end + end + + for j in 1:length(rhos) + @printf("% .15e % .15e % .15e\n",rhos[j],es[j],err[j]) + end +end + +# compute u(x) +function simpleq_hardcore_ux(rho,taus,P,N,J,maxiter,tolerance) + # initialize vectors + (weights,weights_gL,r,T)=simpleq_hardcore_init(taus,P,N,J) + + # initial guess + u0=Array{Float64}(undef,N*J) + for i in 1:N*J + u0[i]=1/(1+r[i]^2)^2 + end + e0=pi*rho + + (u,e,err)=simpleq_hardcore_hatu(u0,e0,rho,r,taus,T,weights,weights_gL,P,N,J,maxiter,tolerance) + + for i in 1:N*J + @printf("% .15e % .15e\n",r[i],u[i]) + end +end + +# compute condensate fraction as a function of rho +function simpleq_hardcore_condensate_fraction_rho(rhos,taus,P,N,J,maxiter,tolerance) + ## spawn workers + # number of workers + nw=nworkers() + # split jobs among workers + work=Array{Array{Int64,1},1}(undef,nw) + # init empty arrays + for p in 1:nw + work[p]=zeros(0) + end + for j in 1:length(rhos) + append!(work[j%nw+1],j) + end + + # initialize vectors + (weights,weights_gL,r,T)=simpleq_hardcore_init(taus,P,N,J) + + # initial guess + u0s=Array{Array{Float64}}(undef,length(rhos)) + e0s=Array{Float64}(undef,length(rhos)) + for j in 1:length(rhos) + u0s[j]=Array{Float64}(undef,N*J) + for i in 1:N*J + u0s[j][i]=1/(1+r[i]^2)^2 + end + e0s[j]=pi*rhos[j] + end + + + # save result from each task + us=Array{Array{Float64}}(undef,length(rhos)) + es=Array{Float64}(undef,length(rhos)) + err=Array{Float64}(undef,length(rhos)) + + count=0 + # for each worker + @sync for p in 1:nw + # for each task + @async for j in work[p] + count=count+1 + if count>=nw + progress(count,length(rhos),10000) + end + # run the task + (us[j],es[j],err[j])=remotecall_fetch(simpleq_hardcore_hatu,workers()[p],u0s[j],e0s[j],rhos[j],r,taus,T,weights,weights_gL,P,N,J,maxiter,tolerance) + end + end + + for j in 1:length(rhos) + du=-inv(simpleq_hardcore_DXi(us[j],es[j],rhos[j],r,taus,T,weights,weights_gL,P,N,J))*simpleq_hardcore_dXidmu(us[j],es[j],rhos[j],r,taus,T,weights,weights_gL,P,N,J) + @printf("% .15e % .15e % .15e\n",rhos[j],du[N*J+1],err[j]) + end +end + + +# initialize computation +@everywhere function simpleq_hardcore_init(taus,P,N,J) + # Gauss-Legendre weights + weights=gausslegendre(N) + weights_gL=gausslaguerre(N) + + # r + r=Array{Float64}(undef,J*N) + for zeta in 0:J-1 + for j in 1:N + xj=weights[1][j] + # set kj + r[zeta*N+j]=(2+(taus[zeta+2]-taus[zeta+1])*sin(pi*xj/2)-(taus[zeta+2]+taus[zeta+1]))/(2-(taus[zeta+2]-taus[zeta+1])*sin(pi*xj/2)+taus[zeta+2]+taus[zeta+1]) + end + end + + # Chebyshev polynomials + T=chebyshev_polynomials(P) + + return (weights,weights_gL,r,T) +end + +# compute u using chebyshev expansions +@everywhere function simpleq_hardcore_hatu(u0,e0,rho,r,taus,T,weights,weights_gL,P,N,J,maxiter,tolerance) + # init + vec=Array{Float64}(undef,J*N+1) + for i in 1:J*N + vec[i]=u0[i] + end + vec[J*N+1]=e0 + + # quantify relative error + error=Inf + + # run Newton algorithm + for i in 1:maxiter-1 + u=vec[1:J*N] + e=vec[J*N+1] + new=vec-inv(simpleq_hardcore_DXi(u,e,rho,r,taus,T,weights,weights_gL,P,N,J))*simpleq_hardcore_Xi(u,e,rho,r,taus,T,weights,weights_gL,P,N,J) + + error=norm(new-vec)/norm(new) + if(error=0 ? 1 : -1) + end + # de/du + out[J*N+1,zetapp*N+n]=2*pi*rho* + (-dot(Fone,gamma1(e,rho,taus,T,weights,weights_gL,P,J,4))-2*dot(FU,gamma2(e,rho,taus,T,weights,weights_gL,P,J,4)*Fone))/denom- + 2*pi*rho* + ((1+2*sqrt(abs(e)))-gamma0(e,rho,weights)-dot(FU,gamma1(e,rho,taus,T,weights,weights_gL,P,J,4))-dot(FU,gamma2(e,rho,taus,T,weights,weights_gL,P,J,4)*FU))* + rho^2*(dot(Fone,gammabar1(taus,T,weights,P,J,4))+2*dot(FU,gammabar2(taus,T,weights,P,J,4)*Fone))/denom^2 + end + end + #de/de + out[J*N+1,J*N+1]=-1+2*pi*rho* + (2-dsedgamma0(e,rho,weights)-dot(FU,dsedgamma1(e,rho,taus,T,weights,weights_gL,P,J,4))-dot(FU,dsedgamma2(e,rho,taus,T,weights,weights_gL,P,J,4)*FU))/denom/ + (2*sqrt(abs(e)))*(e>=0 ? 1 : -1) + + return out +end + +# dXi/dmu +@everywhere function simpleq_hardcore_dXidmu(u,e,rho,r,taus,T,weights,weights_gL,P,N,J) + out=Array{Float64}(undef,J*N+1) + FU=chebyshev(u,taus,weights,P,N,J,4) + + #D's + dsmud0=dsmuD0(e,rho,r,weights,N,J) + dsmud1=dsmuD1(e,rho,r,taus,T,weights,weights_gL,P,N,J,4) + dsmud2=dsmuD2(e,rho,r,taus,T,weights,weights_gL,P,N,J,4) + + # u + for i in 1:J*N + out[i]=(dsmud0[i]+dot(FU,dsmud1[i])+dot(FU,dsmud2[i]*FU))/(4*sqrt(abs(e))) + end + # e + out[J*N+1]=2*pi*rho*(2/3+1/(2*sqrt(abs(e)))- + (dsmudgamma0(e,rho,weights)+dot(FU,dsmudgamma1(e,rho,taus,T,weights,weights_gL,P,J,4))+dot(FU,dsmudgamma2(e,rho,taus,T,weights,weights_gL,P,J,4)*FU))/(4*sqrt(abs(e))) + )/(1-8/3*pi*rho+rho^2*(gammabar0(weights)+dot(FU,gammabar1(taus,T,weights,P,J,4))+dot(FU,gammabar2(taus,T,weights,P,J,4)*FU))) + + return out +end + +# B's +@everywhere function B0(r) + return pi/12*(r-1)^2*(r+5) +end +@everywhere function B1(r,zeta,n,taus,T,weights,nu) + return (taus[zeta+1]>=(2-r)/r || taus[zeta+2]<=-r/(r+2) ? 0 : + 8*pi/(r+1)*integrate_legendre(tau-> + (1-(r-(1-tau)/(1+tau))^2)/(1+tau)^(3-nu)*T[n+1]((2*tau-(taus[zeta+1]+taus[zeta+2]))/(taus[zeta+2]-taus[zeta+1])),max(taus[zeta+1],-r/(r+2)) + ,min(taus[zeta+2],(2-r)/r),weights) + ) +end +@everywhere function B2(r,zeta,n,zetap,m,taus,T,weights,nu) + return 32*pi/(r+1)*integrate_legendre(tau-> + 1/(1+tau)^(3-nu)*T[n+1]((2*tau-(taus[zeta+1]+taus[zeta+2]))/(taus[zeta+2]-taus[zeta+1]))* + (taus[zetap+1]>=alphap(abs(r-(1-tau)/(1+tau))-2*tau/(1+tau),tau) || taus[zetap+2]<=alpham(1+r,tau) ? 0 : + integrate_legendre(s-> + 1/(1+s)^(3-nu)*T[m+1]((2*s-(taus[zetap+1]+taus[zetap+2]))/(taus[zetap+2]-taus[zetap+1])),max(taus[zetap+1] + ,alpham(1+r,tau)),min(taus[zetap+2],alphap(abs(r-(1-tau)/(1+tau))-2*tau/(1+tau),tau)),weights) + ) + ,taus[zeta+1],taus[zeta+2],weights) +end + +# D's +@everywhere function D0(e,rho,r,weights,N,J) + out=Array{Float64}(undef,J*N) + for i in 1:J*N + out[i]=exp(-2*sqrt(abs(e))*r[i])/(r[i]+1)+ + rho*sqrt(abs(e))/(r[i]+1)*integrate_legendre(s-> + (s+1)*B0(s)*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2 + ,0,min(1,r[i]),weights)+ + (r[i]>=1 ? 0 : + rho*sqrt(abs(e))/(2*(r[i]+1))*(1-exp(-4*sqrt(abs(e))*r[i]))*integrate_legendre(s-> + (s+r[i]+1)*B0(s+r[i])*exp(-2*sqrt(abs(e))*s) + ,0,1-r[i],weights) + ) + end + return out +end +@everywhere function D1(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu) + out=Array{Array{Float64}}(undef,J*N) + for i in 1:J*N + out[i]=Array{Float64}(undef,(P+1)*J) + for zeta in 0:J-1 + for n in 0:P + out[i][zeta*(P+1)+n+1]=rho*sqrt(abs(e))/(r[i]+1)*integrate_legendre(s-> + (s+1)*B1(s,zeta,n,taus,T,weights,nu)*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2 + ,0,r[i],weights)+ + rho*sqrt(abs(e))/(2*(r[i]+1))*(1-exp(-4*sqrt(abs(e))*r[i]))*integrate_laguerre(s-> + (s+r[i]+1)*B1(s+r[i],zeta,n,taus,T,weights,nu) + ,2*sqrt(abs(e)),weights_gL) + end + end + end + + return out +end +@everywhere function D2(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu) + out=Array{Array{Float64,2}}(undef,J*N) + for i in 1:J*N + out[i]=Array{Float64,2}(undef,(P+1)*J,(P+1)*J) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[i][zeta*(P+1)+n+1,zetap*(P+1)+m+1]=rho*sqrt(abs(e))/(r[i]+1)*integrate_legendre(s-> + (s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu)*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2 + ,0,r[i],weights)+ + rho*sqrt(abs(e))/(2*(r[i]+1))*(1-exp(-4*sqrt(abs(e))*r[i]))*integrate_laguerre(s-> + (s+r[i]+1)*B2(s+r[i],zeta,n,zetap,m,taus,T,weights,nu) + ,2*sqrt(abs(e)),weights_gL) + end + end + end + end + end + return out +end + +# dD/d sqrt(abs(e))'s +@everywhere function dseD0(e,rho,r,weights,N,J) + out=Array{Float64}(undef,J*N) + for i in 1:J*N + out[i]=-2*r[i]*exp(-2*sqrt(abs(e))*r[i])/(r[i]+1)+ + rho/(r[i]+1)*integrate_legendre(s-> + (s+1)*B0(s)*((1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2) + ,0,min(1,r[i]),weights)+ + (r[i]>=1 ? 0 : + rho/(2*(r[i]+1))*integrate_legendre(s->(s+r[i]+1)*B0(s+r[i])*((1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))*exp(-2*sqrt(abs(e))*s)+4*sqrt(abs(e))*r[i]*exp(-2*sqrt(abs(e))*(2*r[i]+s))),0,1-r[i],weights) + ) + end + return out +end +@everywhere function dseD1(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu) + out=Array{Array{Float64}}(undef,J*N) + for i in 1:J*N + out[i]=Array{Float64}(undef,(P+1)*J) + for zeta in 0:J-1 + for n in 0:P + out[i][zeta*(P+1)+n+1]=rho/(r[i]+1)*integrate_legendre(s-> + (s+1)*B1(s,zeta,n,taus,T,weights,nu)*((1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2) + ,0,r[i],weights)+ + rho/(2*(r[i]+1))*integrate_laguerre(s-> + (s+r[i]+1)*B1(s+r[i],zeta,n,taus,T,weights,nu)*((1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i])) + ,2*sqrt(abs(e)),weights_gL) + end + end + end + return out +end +@everywhere function dseD2(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu) + out=Array{Array{Float64,2}}(undef,J*N) + for i in 1:J*N + out[i]=Array{Float64,2}(undef,(P+1)*J,(P+1)*J) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[i][zeta*(P+1)+n+1,zetap*(P+1)+m+1]=rho/(r[i]+1)*integrate_legendre(s-> + (s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu)*((1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2) + ,0,r[i],weights)+ + rho/(2*(r[i]+1))*integrate_laguerre(s-> + (s+r[i]+1)*B2(s+r[i],zeta,n,zetap,m,taus,T,weights,nu)*((1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i])) + ,2*sqrt(abs(e)),weights_gL) + end + end + end + end + end + return out +end + +# dD/d sqrt(abs(e+mu/2))'s +@everywhere function dsmuD0(e,rho,r,weights,N,J) + out=Array{Float64}(undef,J*N) + for i in 1:J*N + out[i]=-2*r[i]*exp(-2*sqrt(abs(e))*r[i])/(r[i]+1)+ + rho/(r[i]+1)*integrate_legendre(s-> + (s+1)*B0(s)*((-1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2) + ,0,min(1,r[i]),weights)+ + (r[i]>=1 ? 0 : + rho/(2*(r[i]+1))*integrate_legendre(s->(s+r[i]+1)*B0(s+r[i])*((-1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))*exp(-2*sqrt(abs(e))*s)+4*sqrt(abs(e))*r[i]*exp(-2*sqrt(abs(e))*(2*r[i]+s))),0,1-r[i],weights) + ) + end + return out +end +@everywhere function dsmuD1(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu) + out=Array{Array{Float64}}(undef,J*N) + for i in 1:J*N + out[i]=Array{Float64}(undef,(P+1)*J) + for zeta in 0:J-1 + for n in 0:P + out[i][zeta*(P+1)+n+1]=rho/(r[i]+1)*integrate_legendre(s-> + (s+1)*B1(s,zeta,n,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2) + ,0,r[i],weights)+ + rho/(2*(r[i]+1))*integrate_laguerre(s-> + (s+r[i]+1)*B1(s+r[i],zeta,n,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i])) + ,2*sqrt(abs(e)),weights_gL) + end + end + end + return out +end +@everywhere function dsmuD2(e,rho,r,taus,T,weights,weights_gL,P,N,J,nu) + out=Array{Array{Float64,2}}(undef,J*N) + for i in 1:J*N + out[i]=Array{Float64,2}(undef,(P+1)*J,(P+1)*J) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[i][zeta*(P+1)+n+1,zetap*(P+1)+m+1]=rho/(r[i]+1)*integrate_legendre(s-> + (s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*r[i])*(exp(-2*sqrt(abs(e))*(r[i]-s))-exp(-2*sqrt(abs(e))*(r[i]+s)))/2+2*sqrt(abs(e))*s*(exp(-2*sqrt(abs(e))*(r[i]-s))+exp(-2*sqrt(abs(e))*(r[i]+s)))/2) + ,0,r[i],weights)+ + rho/(2*(r[i]+1))*integrate_laguerre(s-> + (s+r[i]+1)*B2(s+r[i],zeta,n,zetap,m,taus,T,weights,nu)*((-1-2*sqrt(abs(e))*s)*(1-exp(-4*sqrt(abs(e))*r[i]))+4*sqrt(abs(e))*r[i]*exp(-4*sqrt(abs(e))*r[i])) + ,2*sqrt(abs(e)),weights_gL) + end + end + end + end + end + return out +end + +# gamma's +@everywhere function gamma0(e,rho,weights) + return 2*rho*e*integrate_legendre(s->(s+1)*B0(s)*exp(-2*sqrt(abs(e))*s),0,1,weights) +end +@everywhere function gamma1(e,rho,taus,T,weights,weights_gL,P,J,nu) + out=Array{Float64}(undef,J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + out[zeta*(P+1)+n+1]=2*rho*e*integrate_laguerre(s->(s+1)*B1(s,zeta,n,taus,T,weights,nu),2*sqrt(abs(e)),weights_gL) + end + end + return out +end +@everywhere function gamma2(e,rho,taus,T,weights,weights_gL,P,J,nu) + out=Array{Float64,2}(undef,J*(P+1),J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[zeta*(P+1)+n+1,zetap*(P+1)+m+1]=2*rho*e*integrate_laguerre(s->(s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu),2*sqrt(abs(e)),weights_gL) + end + end + end + end + return out +end + +# dgamma/d sqrt(abs(e))'s +@everywhere function dsedgamma0(e,rho,weights) + return 4*rho*sqrt(abs(e))*integrate_legendre(s->(s+1)*B0(s)*(1-sqrt(abs(e))*s)*exp(-2*sqrt(abs(e))*s),0,1,weights) +end +@everywhere function dsedgamma1(e,rho,taus,T,weights,weights_gL,P,J,nu) + out=Array{Float64}(undef,J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + out[zeta*(P+1)+n+1]=4*rho*e*integrate_laguerre(s->(s+1)*B1(s,zeta,n,taus,T,weights,nu)*(1-sqrt(abs(e))*s),2*sqrt(abs(e)),weights_gL) + end + end + return out +end +@everywhere function dsedgamma2(e,rho,taus,T,weights,weights_gL,P,J,nu) + out=Array{Float64,2}(undef,J*(P+1),J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[zeta*(P+1)+n+1,zetap*(P+1)+m+1]=4*rho*e*integrate_laguerre(s->(s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu)*(1-sqrt(abs(e))*s),2*sqrt(abs(e)),weights_gL) + end + end + end + end + return out +end + +# dgamma/d sqrt(e+mu/2)'s +@everywhere function dsmudgamma0(e,rho,weights) + return -4*rho*e*integrate_legendre(s->(s+1)*s*B0(s)*exp(-2*sqrt(abs(e))*s),0,1,weights) +end +@everywhere function dsmudgamma1(e,rho,taus,T,weights,weights_gL,P,J,nu) + out=Array{Float64}(undef,J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + out[zeta*(P+1)+n+1]=-4*rho*e*integrate_laguerre(s->s*(s+1)*B1(s,zeta,n,taus,T,weights,nu),2*sqrt(abs(e)),weights_gL) + end + end + return out +end +@everywhere function dsmudgamma2(e,rho,taus,T,weights,weights_gL,P,J,nu) + out=Array{Float64,2}(undef,J*(P+1),J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[zeta*(P+1)+n+1,zetap*(P+1)+m+1]=-4*rho*e*integrate_laguerre(s->s*(s+1)*B2(s,zeta,n,zetap,m,taus,T,weights,nu),2*sqrt(abs(e)),weights_gL) + end + end + end + end + return out +end + +# \bar gamma's +@everywhere function gammabar0(weights) + return 4*pi*integrate_legendre(s->s^2*B0(s-1),0,1,weights) +end +@everywhere function gammabar1(taus,T,weights,P,J,nu) + out=Array{Float64}(undef,J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + out[zeta*(P+1)+n+1]=4*pi*integrate_legendre(s->s^2*B1(s-1,zeta,n,taus,T,weights,nu),0,1,weights) + end + end + return out +end +@everywhere function gammabar2(taus,T,weights,P,J,nu) + out=Array{Float64,2}(undef,J*(P+1),J*(P+1)) + for zeta in 0:J-1 + for n in 0:P + for zetap in 0:J-1 + for m in 0:P + out[zeta*(P+1)+n+1,zetap*(P+1)+m+1]=4*pi*integrate_legendre(s->s^2*B2(s-1,zeta,n,zetap,m,taus,T,weights,nu),0,1,weights) + end + end + end + end + return out +end diff --git a/src/simpleq-iteration.jl b/src/simpleq-iteration.jl new file mode 100644 index 0000000..98977b8 --- /dev/null +++ b/src/simpleq-iteration.jl @@ -0,0 +1,94 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# compute rho(e) using the iteration +function simpleq_iteration_rho_e(es,order,v,maxiter) + for j in 1:length(es) + (u,rho)=simpleq_iteration_hatun(es[j],order,v,maxiter) + @printf("% .15e % .15e\n",es[j],real(rho[maxiter+1])) + end +end + +# compute u(x) using the iteration and print at every step +function simpleq_iteration_ux(order,e,v,maxiter,xmin,xmax,nx) + (u,rho)=simpleq_iteration_hatun(e,order,v,maxiter) + + weights=gausslegendre(order) + for i in 1:nx + x=xmin+(xmax-xmin)*i/nx + @printf("% .15e ",x) + for n in 2:maxiter+1 + @printf("% .15e ",real(easyeq_u_x(x,u[n],weights))) + end + print('\n') + end +end + + +# \hat u_n +function simpleq_iteration_hatun(e,order,v,maxiter) + # gauss legendre weights + weights=gausslegendre(order) + + # initialize V and Eta + (V,V0)=easyeq_init_v(weights,v) + (Eta,Eta0)=easyeq_init_H(weights,v) + + # init u and rho + u=Array{Array{Float64}}(undef,maxiter+1) + u[1]=zeros(Float64,order) + rho=zeros(Float64,maxiter+1) + + # iterate + for n in 1:maxiter + u[n+1]=simpleq_iteration_A(e,weights,Eta)\simpleq_iteration_b(u[n],e,rho[n],V) + rho[n+1]=simpleq_iteration_rhon(u[n+1],e,weights,V0,Eta0) + end + + return (u,rho) +end + +# A +function simpleq_iteration_A(e,weights,Eta) + N=length(weights[1]) + out=zeros(Float64,N,N) + for i in 1:N + k=(1-weights[1][i])/(1+weights[1][i]) + out[i,i]=k^2+4*e + for j in 1:N + y=(weights[1][j]+1)/2 + out[i,j]+=weights[2][j]*(1-y)*Eta[i][j]/(2*(2*pi)^3*y^3) + end + end + return out +end + +# b +function simpleq_iteration_b(u,e,rho,V) + out=zeros(Float64,length(V)) + for i in 1:length(V) + out[i]=V[i]+2*e*rho*u[i]^2 + end + return out +end + +# rho_n +function simpleq_iteration_rhon(u,e,weights,V0,Eta0) + S=V0 + for i in 1:length(weights[1]) + y=(weights[1][i]+1)/2 + S+=-weights[2][i]*(1-y)*u[i]*Eta0[i]/(2*(2*pi)^3*y^3) + end + return 2*e/S +end diff --git a/src/tools.jl b/src/tools.jl new file mode 100644 index 0000000..0d3dc7f --- /dev/null +++ b/src/tools.jl @@ -0,0 +1,49 @@ +## Copyright 2021 Ian Jauslin +## +## Licensed under the Apache License, Version 2.0 (the "License"); +## you may not use this file except in compliance with the License. +## You may obtain a copy of the License at +## +## http://www.apache.org/licenses/LICENSE-2.0 +## +## Unless required by applicable law or agreed to in writing, software +## distributed under the License is distributed on an "AS IS" BASIS, +## WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +## See the License for the specific language governing permissions and +## limitations under the License. + +# \Phi(x)=2*(1-sqrt(1-x))/x +@everywhere function Phi(x) + if abs(x)>1e-5 + return 2*(1-sqrt(abs(1-x)))/x + else + return 1+x/4+x^2/8+5*x^3/64+7*x^4/128+21*x^5/512 + end +end +# \partial\Phi +@everywhere function dPhi(x) + #if abs(x-1)<1e-5 + # @printf(stderr,"warning: dPhi is singular at 1, and evaluating it at (% .8e+i% .8e)\n",real(x),imag(x)) + #end + if abs(x)>1e-5 + return 1/(sqrt(abs(1-x))*x)*(1-x>=0 ? 1 : -1)-2*(1-sqrt(abs(1-x)))/x^2 + else + return 1/4+x/4+15*x^2/64+7*x^3/32+105*x^4/512+99*x^5/512 + end +end + +# apply Phi to every element of a vector +@everywhere function dotPhi(v) + out=zeros(Float64,length(v)) + for i in 1:length(v) + out[i]=Phi(v[i]) + end + return out +end +@everywhere function dotdPhi(v) + out=zeros(Float64,length(v)) + for i in 1:length(v) + out[i]=dPhi(v[i]) + end + return out +end