From 8ed03913fc9e5828fa913ad24711acb60349581f Mon Sep 17 00:00:00 2001 From: vfisikop Date: Tue, 12 Mar 2024 17:45:22 +0200 Subject: [PATCH] Add JOSS paper --- .github/workflows/R-CMD-check-macOS.yml | 62 ----------- .github/workflows/R-CMD-check-ubuntu.yml | 65 ------------ .github/workflows/R-CMD-check-windows.yml | 61 ----------- .github/workflows/cmake-clang.yml | 39 ------- .github/workflows/cmake-examples.yml | 40 ------- .github/workflows/cmake-gcc.yml | 38 ------- .github/workflows/docs.yml | 27 ----- .github/workflows/draft_pdf.yml | 24 +++++ joss_paper/paper.md | 121 ---------------------- joss_paper/paper.bib => paper.bib | 0 paper.md | 108 +++++++++++++++++++ 11 files changed, 132 insertions(+), 453 deletions(-) delete mode 100644 .github/workflows/R-CMD-check-macOS.yml delete mode 100644 .github/workflows/R-CMD-check-ubuntu.yml delete mode 100644 .github/workflows/R-CMD-check-windows.yml delete mode 100644 .github/workflows/cmake-clang.yml delete mode 100644 .github/workflows/cmake-examples.yml delete mode 100644 .github/workflows/cmake-gcc.yml delete mode 100644 .github/workflows/docs.yml create mode 100644 .github/workflows/draft_pdf.yml delete mode 100644 joss_paper/paper.md rename joss_paper/paper.bib => paper.bib (100%) create mode 100644 paper.md diff --git a/.github/workflows/R-CMD-check-macOS.yml b/.github/workflows/R-CMD-check-macOS.yml deleted file mode 100644 index b1ac281c3..000000000 --- a/.github/workflows/R-CMD-check-macOS.yml +++ /dev/null @@ -1,62 +0,0 @@ -############################################################################## -# GitHub Actions Workflow to test the R interface of volesti -# -# Copyright (c) 2020 Vissarion Fisikopoulos -# -# Licensed under GNU LGPL.3, see LICENCE file -############################################################################## - -on: [push, pull_request] - -name: R-CMD-check-macOS - -jobs: - R-CMD-check: - runs-on: ${{ matrix.config.os }} - - name: ${{ matrix.config.os }} (${{ matrix.config.r }}) - - strategy: - fail-fast: false - matrix: - config: - - {os: macOS-latest, r: '4.1.2'} -# - {os: macOS-latest, r: 'devel'} # Error in library(devtools) : there is no package called ‘devtools’ - - {os: macOS-latest, r: 'release'} - - env: - R_REMOTES_NO_ERRORS_FROM_WARNINGS: true - RSPM: ${{ matrix.config.rspm }} - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - - steps: - - uses: actions/checkout@v3 - - - uses: r-lib/actions/setup-r@v2 - with: - r-version: ${{ matrix.config.r }} - - - uses: r-lib/actions/setup-pandoc@v2 - - - name: Install dependencies - run: Rscript -e "install.packages(c('devtools', dependencies=TRUE))" -e "install.packages(c('rcmdcheck', 'devtools', 'Rcpp', 'RcppEigen', 'BH', 'testthat', 'downloader', 'xfun'))"; - - - name: Checkout Rvolesti repository - run: git clone https://github.com/GeomScale/Rvolesti.git; - - - name: Upgrade with current develop of volesti - run: cp -rf include Rvolesti/src/volesti; - cp -rf external Rvolesti/src; - - - name: Check - env: - _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: cd Rvolesti/; - Rscript -e "library(rcmdcheck)" -e "rcmdcheck::rcmdcheck(args = c('--no-manual'), error_on = 'warning', check_dir = 'check')" - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@v2 - with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check diff --git a/.github/workflows/R-CMD-check-ubuntu.yml b/.github/workflows/R-CMD-check-ubuntu.yml deleted file mode 100644 index 753ee114a..000000000 --- a/.github/workflows/R-CMD-check-ubuntu.yml +++ /dev/null @@ -1,65 +0,0 @@ -############################################################################## -# GitHub Actions Workflow to test the R interface of volesti -# -# Copyright (c) 2020 Vissarion Fisikopoulos -# -# Licensed under GNU LGPL.3, see LICENCE file -############################################################################## - -on: [push, pull_request] - -name: R-CMD-check-ubuntu - -jobs: - R-CMD-check: - runs-on: ${{ matrix.config.os }} - - name: ${{ matrix.config.os }} (${{ matrix.config.r }}) - - strategy: - fail-fast: false - matrix: - config: - - {os: ubuntu-latest, r: 'devel'} - - {os: ubuntu-latest, r: 'release'} - - {os: ubuntu-20.04, r: 'devel'} - - {os: ubuntu-20.04, r: 'release'} - - env: - R_REMOTES_NO_ERRORS_FROM_WARNINGS: true - RSPM: ${{ matrix.config.rspm }} - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - - steps: - - uses: actions/checkout@v3 - - run: sudo apt-get update || true; - sudo apt install build-essential libcurl4-gnutls-dev libxml2-dev libssl-dev libgit2-dev libfontconfig1-dev libharfbuzz-dev libfribidi-dev; - - - uses: r-lib/actions/setup-r@v2 - with: - r-version: ${{ matrix.config.r }} - - - uses: r-lib/actions/setup-pandoc@v2 - - - name: Install dependencies - run: Rscript -e "install.packages(c('testthat', 'pkgload', 'rcmdcheck', 'devtools', 'Rcpp', 'RcppEigen', 'BH', 'downloader', 'xfun', dependencies=TRUE))"; - - - name: Checkout Rvolesti repository - run: git clone https://github.com/GeomScale/Rvolesti.git; - - - name: Upgrade with current develop of volesti - run: cp -rf include Rvolesti/src/volesti; - cp -rf external Rvolesti/src; - - - name: Check - env: - _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: cd Rvolesti/; - Rscript -e "library(rcmdcheck)" -e "rcmdcheck::rcmdcheck(args = c('--no-manual'), error_on = 'warning', check_dir = 'check')" - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@v2 - with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check diff --git a/.github/workflows/R-CMD-check-windows.yml b/.github/workflows/R-CMD-check-windows.yml deleted file mode 100644 index 97e725aa0..000000000 --- a/.github/workflows/R-CMD-check-windows.yml +++ /dev/null @@ -1,61 +0,0 @@ -############################################################################## -# GitHub Actions Workflow to test the R interface of volesti -# -# Copyright (c) 2020 Vissarion Fisikopoulos -# -# Licensed under GNU LGPL.3, see LICENCE file -############################################################################## - -on: [push, pull_request] - -name: R-CMD-check-windows - -jobs: - R-CMD-check: - runs-on: ${{ matrix.config.os }} - - name: ${{ matrix.config.os }} (${{ matrix.config.r }}) - - strategy: - fail-fast: false - matrix: - config: -# - {os: windows-latest, r: 'devel'} # Error in library(devtools) : there is no package called ‘devtools’ - - {os: windows-latest, r: 'release'} - - env: - R_REMOTES_NO_ERRORS_FROM_WARNINGS: true - RSPM: ${{ matrix.config.rspm }} - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - - steps: - - uses: actions/checkout@v3 - - - uses: r-lib/actions/setup-r@v2 - with: - r-version: ${{ matrix.config.r }} - - - uses: r-lib/actions/setup-pandoc@v2 - - - name: Install dependencies - run: Rscript -e "install.packages(c('devtools', dependencies=TRUE))" -e "install.packages(c('rcmdcheck', 'devtools', 'Rcpp', 'RcppEigen', 'BH', 'testthat', 'downloader', 'xfun'))" - - - name: Checkout Rvolesti repository - run: git clone https://github.com/GeomScale/Rvolesti.git; - - - name: Upgrade with current develop of volesti - run: cp -r -fo include Rvolesti/src/volesti; - cp -r -fo external Rvolesti/src; - - - name: Check - env: - _R_CHECK_CRAN_INCOMING_REMOTE_: false - run: cd Rvolesti/; - Rscript -e "library(rcmdcheck)" -e "rcmdcheck::rcmdcheck(args = c('--no-manual'), error_on = 'warning', check_dir = 'check')" - - - name: Upload check results - if: failure() - uses: actions/upload-artifact@v2 - with: - name: ${{ runner.os }}-r${{ matrix.config.r }}-results - path: check diff --git a/.github/workflows/cmake-clang.yml b/.github/workflows/cmake-clang.yml deleted file mode 100644 index 520b33159..000000000 --- a/.github/workflows/cmake-clang.yml +++ /dev/null @@ -1,39 +0,0 @@ -############################################################################## -# GitHub Actions Workflow for volesti to build tests with Clang -# -# Copyright (c) 2020-2022 Vissarion Fisikopoulos -# -# Licensed under GNU LGPL.3, see LICENCE file -############################################################################## -name: cmake-clang - -on: [push, pull_request] - -jobs: - build: - name: ${{ matrix.config.os }} - ${{ matrix.config.compiler }} - strategy: - fail-fast: false - matrix: - config: - - {os: ubuntu-22.04, compiler_pkg: clang-11, compiler: clang++-11} - - {os: ubuntu-22.04, compiler_pkg: clang-12, compiler: clang++-12} - - {os: ubuntu-22.04, compiler_pkg: clang-13, compiler: clang++-13} - - {os: ubuntu-22.04, compiler_pkg: clang-14, compiler: clang++-14} - #- {os: ubuntu-22.04, compiler_pkg: clang-15, compiler: clang++-15} - - {os: ubuntu-20.04, compiler_pkg: clang-8, compiler: clang++-8} - - {os: ubuntu-20.04, compiler_pkg: clang-9, compiler: clang++-9} - - {os: ubuntu-20.04, compiler_pkg: clang-10, compiler: clang++-10} - - {os: ubuntu-20.04, compiler_pkg: clang-11, compiler: clang++-11} - - {os: ubuntu-20.04, compiler_pkg: clang-12, compiler: clang++-12} - runs-on: ${{ matrix.config.os }} - steps: - - uses: actions/checkout@v1 - - run: sudo apt-get update || true; - sudo apt-get install ${{ matrix.config.compiler_pkg }} lp-solve libopenblas-dev; - rm -rf build; - mkdir build; - cd build; - cmake -D CMAKE_CXX_COMPILER=${{ matrix.config.compiler }} -D CMAKE_CXX_FLAGS=-fsanitize=memory -D CMAKE_CXX_FLAGS=-fsanitize=undefined -D CMAKE_CXX_FLAGS=-g -D DISABLE_NLP_ORACLES=ON -D USE_MKL=OFF ../test; - make; - ctest --verbose; \ No newline at end of file diff --git a/.github/workflows/cmake-examples.yml b/.github/workflows/cmake-examples.yml deleted file mode 100644 index b7784fa30..000000000 --- a/.github/workflows/cmake-examples.yml +++ /dev/null @@ -1,40 +0,0 @@ -############################################################################## -# GitHub Actions Workflow for volesti to build tests with GCC -# -# Copyright (c) 2020-2022 Vissarion Fisikopoulos -# -# Licensed under GNU LGPL.3, see LICENCE file -############################################################################## -name: cmake-examples - -on: [push, pull_request] - -jobs: - build: - name: ${{ matrix.config.os }} - ${{ matrix.config.compiler }} - strategy: - fail-fast: false - matrix: - config: - - {os: ubuntu-22.04, compiler_pkg: clang-11, compiler: clang++-11} - - {os: ubuntu-22.04, compiler_pkg: g++-11, compiler: g++-11} - runs-on: ${{ matrix.config.os }} - steps: - - uses: actions/checkout@v1 - - run: sudo apt-get update || true; - sudo apt-get install ${{ matrix.config.compiler_pkg }} lp-solve libomp-dev libopenblas-dev libarpack2-dev; - - name: Build examples - run: | - cd examples - for dir in */; do - if [ "$dir" != "EnvelopeProblemSOS/" ] && [ "$dir" != "python_utilities/" ]; then - echo - echo "Building examples in $dir ....................." - cd "$dir" - mkdir build && cd build - cmake -DCMAKE_CXX_COMPILER=${{ matrix.config.compiler }} -DUSE_MKL=OFF .. - make - cd ../.. - fi - done - diff --git a/.github/workflows/cmake-gcc.yml b/.github/workflows/cmake-gcc.yml deleted file mode 100644 index f18e358ac..000000000 --- a/.github/workflows/cmake-gcc.yml +++ /dev/null @@ -1,38 +0,0 @@ -############################################################################## -# GitHub Actions Workflow for volesti to build tests with GCC -# -# Copyright (c) 2020-2021 Vissarion Fisikopoulos -# -# Licensed under GNU LGPL.3, see LICENCE file -############################################################################## -name: cmake-gcc - -on: [push, pull_request] - -jobs: - build: - name: ${{ matrix.config.os }} - ${{ matrix.config.compiler }} - strategy: - fail-fast: false - matrix: - config: - - {os: ubuntu-22.04, compiler: g++-9} - - {os: ubuntu-22.04, compiler: g++-10} - - {os: ubuntu-22.04, compiler: g++-11} - - {os: ubuntu-22.04, compiler: g++-12} - #- {os: ubuntu-22.04, compiler: g++-13} - - {os: ubuntu-20.04, compiler: g++-7} - - {os: ubuntu-20.04, compiler: g++-8} - - {os: ubuntu-20.04, compiler: g++-9} - - {os: ubuntu-20.04, compiler: g++-10} - runs-on: ${{ matrix.config.os }} - steps: - - uses: actions/checkout@v1 - - run: sudo apt-get update || true; - sudo apt-get install ${{ matrix.config.compiler }} lp-solve libopenblas-dev; - rm -rf build; - mkdir build; - cd build; - cmake -D CMAKE_CXX_COMPILER=${{ matrix.config.compiler }} -D DISABLE_NLP_ORACLES=ON -D USE_MKL=OFF ../test; - make; - ctest --verbose; diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml deleted file mode 100644 index fca7ede90..000000000 --- a/.github/workflows/docs.yml +++ /dev/null @@ -1,27 +0,0 @@ -name: Docs -on: [push, pull_request] - -jobs: - notebooks: - name: "Build the docs" - runs-on: ubuntu-latest - steps: - - uses: actions/checkout@v2 - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: 3.8 - - - name: Install dependencies - run: | - sudo apt-get install doxygen - python -m pip install -U pip - python -m pip install -r docs/requirements.txt - - - name: Build the docs - run: | - python -m pip install -U setuptools - python -m pip install -U pillow mock alabaster commonmark recommonmark sphinx sphinx-rtd-theme readthedocs-sphinx-ext - cd docs - python -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html diff --git a/.github/workflows/draft_pdf.yml b/.github/workflows/draft_pdf.yml new file mode 100644 index 000000000..1d7dc44e4 --- /dev/null +++ b/.github/workflows/draft_pdf.yml @@ -0,0 +1,24 @@ +name: Draft PDF +on: [push] + +jobs: + paper: + runs-on: ubuntu-latest + name: Paper Draft + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Build draft PDF + uses: openjournals/openjournals-draft-action@master + with: + journal: joss + # This should be the path to the paper within your repo. + paper-path: paper.md + - name: Upload + uses: actions/upload-artifact@v4 + with: + name: paper + # This is the output path where Pandoc will write the compiled + # PDF. Note, this should be the same directory as the input + # paper.md + path: paper.pdf diff --git a/joss_paper/paper.md b/joss_paper/paper.md deleted file mode 100644 index 06b30ef4a..000000000 --- a/joss_paper/paper.md +++ /dev/null @@ -1,121 +0,0 @@ ---- -title: 'volesti: C++ library for sampling and volume computation on convex bodies' -tags: - - C++ - - geometry - - randomization - - Monte-Carlo methods - - convexity -authors: - - name: Apostolos Chalkis - orcid: 0000-0000-0000-0000 - equal-contrib: true - affiliation: "2, 4" # (Multiple affiliations must be quoted) - - name: Vissarion Fisikopoulos - corresponding: true # (This is how to denote the corresponding author) - equal-contrib: true # (This is how you can denote equal contributions between multiple authors) - affiliation: "1, 4" - - name: Marios Papachristou - equal-contrib: true # (This is how you can denote equal contributions between multiple authors) - affiliation: 5 - - name: Elias Tsigaridas - equal-contrib: true # (This is how you can denote equal contributions between multiple authors) - affiliation: "3, 4" -affiliations: - - name: National & Kapodistrian University of Athens, Greece - index: 1 - - name: Quantagonia - index: 2 - - name: Inria Paris and IMJ-PRG, Sorbonne Universit\`e - index: 3 - - name: GeomScale - index: 4 - - name: Cornell University - index: 5 -date: 11 March 2024 -bibliography: paper.bib - ---- - -# Summary - -Sampling from (constrained) high-dimensional distributions and volume approximation of convex -bodies are fundamental operations that appear in optimization, finance, -engineering, artificial intelligence, and machine learning. -We present `volesti`, a C++ library that delivers efficient implementations of state-of-the-art, mainly randomized, algorithms -to sample from general logconcave distributions. -Based on these routines can estimate the volume of convex bodies in high dimensions, -round them and also compute multidimensional integrals over them. -The backbone of our library consists of Monte-Carlo algorithms, -that are randomized algorithms, the output of which can be incorrect with (usually very small) error probability; thus, we also provide several -high-dimensional statistical tests to certify and verify the output. - -The focus of `volesti`' is scalability in high dimensions, -that, depending on the problem at hand, could be in the order of hundreds or thousands dimension. -Another novelty is the ability to handle a variety of different inputs -for the constrained support of the various distributions. -`volesti` supports three different types of polyhedra [@Ziegler:1995], spectrahedra [@Ramana:1999] -and general non-linear convex objects. - -`volesti` relies on `Eigen` library [@eigen] for linear algebra but also support `MKL` optimizations [@mkl]. -There are R [@Chalkis:2021] and Python [@Chalkis_dingo:2023] interfaces available; -alas not all C++ functionality is available in through these interfaces. - -# Statement of need - -High-dimensional sampling from multivariate distributions with Markov Chain Monte Carlo (MCMC) -algorithms is a fundamental problem with many applications in the whole spectrum of science and engineering [@Iyengar:1988; -@Somerville:1998; @Genz:2009; @Schellenberger:2009]. -In particular, multivariate integration over a convex set -as well as the volume approximation of convex sets -have accumulated a huge amount of effort from theorists and engineers over the last decades. -Nevertheless, these problems are computationally hard for general dimensions [@Dyer:1988]. -MCMC algorithms made remarkable progress -and their use allowed us to efficiently tackle the problems of sampling and -volume estimation of convex bodies in theory, -by the introduction of (ragher sharp) theoretical guarantees [@Chen:2018; @Lee:2018; -@Mangoubi:2019]. -Unfortunately, these theoretical guarantees of the MCMC algorithms -do not extend in an straightforward manner to efficient implementations able to attack problems coming from real-life computations. -Therefore, we witnessed the birth of efficient in practice MCMC algorithm -that they relax the theoretical guarantees and -and employ new algorithmic and statistical techniques -to be amenable to efficient implementations. -Remarkably, these algorithms, and the corresponding implementations, -also meet the requirements for high accuracy results -[@Emiris:2014; @Cousins:2015; @Chalkis_volume:2023; @Kook:2022]. -Let us mention that the volume algorithm of @Cousins:2015 and the sampling method of @Kook:2022 are available as `MATLAB` -packages. - -All aforementioned algorithms and techniques are available in `volesti` -along with the sampling algorithm by -@Chalkis_hmc:2023 and the algorithms for spectrahedra by @Chalkis_spectra:2022. - -The efficient implementations of `volesti -(i) suport various sampling techniques based on geometric walks, roughly speaking these are a continuous version of MCMC algorithms, like Billard walk, Hamiltonian walk and other, -(ii) give us the ability to sample from various distributions, like uniform, log-concave, exponential, and Gaussian, -(iii) allows to consider the distributions - constrained in various convex domains, like hypercubes, zonotopes, general polytopes (in H and V representations), spectrahedra, - and (iv) can perform volume computations, integration, and solve problem from real life applications in very high dimensions. - - - -We use `volesti` extensively in various research and engineering directions that we pursue. -In particular, for the problem of sampling the flux space of metabolic networks -we were able to sample from the most complicated human metabolic network accessible today, Recon3D [@cftz-socg021], -we use to model financial crises [@ccef-crises-j], -to detect low volatility anomalies in stock markets [@bcft-aistats-23], - to introduce randomized control in asset pricing and portfolio performance evaluation [@bcft-arxiv-24]), but also to sample from (and compute the volume of) spectrahedra [@Chalkis_spectra:2022], the feasible regions of semidefinite programs. - -Even more, `volesti` has been used in conducting research in electric power systems [@Venzke:2019], for problems -in probabilistic inference [@Spallitta:2024], -to perform resource analysis on programs [@pham-phd-2024]; -but also to more theoretical and mathematical challenges, like the computation of topological invariants [@co-alenex-2021] - and persistent homology [@vm-fods-2022]. - -# Acknowledgements - -We would like to thank the contributors to the `volesti` library for their valuable contributions and -feedback. - -# References diff --git a/joss_paper/paper.bib b/paper.bib similarity index 100% rename from joss_paper/paper.bib rename to paper.bib diff --git a/paper.md b/paper.md new file mode 100644 index 000000000..41394567c --- /dev/null +++ b/paper.md @@ -0,0 +1,108 @@ +--- +title: 'volesti: A C++ library for sampling and volume computation on convex bodies' +tags: + - C++ + - geometry + - randomization + - Monte Carlo methods + - convexity +authors: + - name: Apostolos Chalkis + orcid: 0000-0000-0000-0000 + equal-contrib: true + affiliation: "2, 4" # (Multiple affiliations must be quoted) + - name: Vissarion Fisikopoulos + corresponding: true # (This is how to denote the corresponding author) + equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + affiliation: "1, 4" + - name: Marios Papachristou + equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + affiliation: 5 + - name: Elias Tsigaridas + equal-contrib: true # (This is how you can denote equal contributions between multiple authors) + affiliation: "3, 4" +affiliations: + - name: National & Kapodistrian University of Athens, Greece + index: 1 + - name: Quantagonia + index: 2 + - name: Inria Paris and IMJ-PRG, Sorbonne Université + index: 3 + - name: GeomScale + index: 4 + - name: Cornell University + index: 5 +date: 11 March 2024 +bibliography: paper.bib + +--- + +# Summary + +Sampling from (constrained) high-dimensional distributions and volume approximation of convex +bodies are fundamental operations that appear in optimization, finance, +engineering, artificial intelligence, and machine learning. +We present `volesti`, a C++ library that delivers efficient implementations of state-of-the-art, mainly randomized, algorithms +to sample from general logconcave distributions. +Based on these routines, we can estimate the volume of convex bodies in high dimensions, +round them, and compute multidimensional integrals over them. +The backbone of our library consists of Monte Carlo algorithms, +which are randomized algorithms, the output of which can be incorrect with (usually very small) error probability; thus, we also provide several +high-dimensional statistical tests to certify and verify the output. + +The focus of `volesti`' is scalability in high dimensions, +that, depending on the problem at hand, could range from hundreds to thousands of dimensions. +Another novelty is the ability to handle a variety of different inputs +for the constrained support of the various distributions. +`volesti` supports three different types of polyhedra [@Ziegler:1995], spectrahedra [@Ramana:1999] +and general non-linear convex objects. + +`volesti` relies on `Eigen` library [@eigen] for linear algebra but also supports `MKL` optimizations [@mkl]. +There are R [@Chalkis:2021] and Python [@Chalkis_dingo:2023] interfaces available. + +# Statement of need + +High-dimensional sampling from multivariate distributions with Markov Chain Monte Carlo (MCMC) +algorithms is a fundamental problem with many applications in science and engineering [@Iyengar:1988; @Somerville:1998; @Genz:2009; @Schellenberger:2009]. +In particular, multivariate integration over a convex set as well as the volume approximation of convex sets have garnered significant attention from theorists and engineers over the last decades. +Nevertheless, these problems are computationally hard for general dimensions [@Dyer:1988]. +MCMC algorithms made remarkable progress and their use allowed us to efficiently tackle the problems of sampling and volume estimation of convex bodies in theory, +by the introduction of (rigorous) theoretical guarantees [@Chen:2018; @Lee:2018; +@Mangoubi:2019]. +Unfortunately, these theoretical guarantees of the MCMC algorithms +do not extend in a straightforward manner to efficient implementations able to attack problems coming from real-life computations. +Therefore, we witnessed the birth of efficient in practice MCMC algorithm +that relax the theoretical guarantees and employ new algorithmic and statistical techniques +to be amenable to efficient implementations. +Remarkably, these algorithms, and the corresponding implementations, +also meet the requirements for high accuracy results +[@Emiris:2014; @Cousins:2015; @Chalkis_volume:2023; @Kook:2022]; +however several existing published methods are available as part of propertiary packages (MATLAB) [@Cousins:2015; @Kook:2022]. + +Our open-source package -- volesti -- offers all of the aforementioned functionality, together with the support of sampling from general log-concave densities @Chalkis_hmc:2023, and uniform sampling from spectrahedra @Chalkis_spectra:2022. + +Our implementation supports: +1. support various sampling techniques based on geometric walks, roughly speaking these are a continuous version of MCMC algorithms, such as Billard walk, Hamiltonian walk and others, +2. give the user the ability to sample from various distributions, like uniform, exponential, Gaussian, and general log-concave densities, +3. allows to consider the distributions constrained in various convex domains, such as hypercubes, zonotopes, general polytopes (defined either as a set of linear inequalities or as a convex hull of a pointset), spectrahedra (feasible sets of semidefinite programs), and, +4. can perform volume computations, integration, and solve problems from real life applications in very high dimensions. + +# Impact + +`volesti` has been used extensively in various research and engineering projects coauthored by the authors of this paper. +In particular, for the problem of sampling the flux space of metabolic networks +we were able to sample from the most complicated human metabolic network accessible today, Recon3D [@cftz-socg021], +used to model financial crises [@ccef-crises-j], +to detect low volatility anomalies in stock markets [@bcft-aistats-23], + to introduce randomized control in asset pricing and portfolio performance evaluation [@bcft-arxiv-24]), but also to sample from (and compute the volume of) spectrahedra [@Chalkis_spectra:2022], the feasible regions of semidefinite programs. + +Even more, `volesti` has been used by other research teams in conducting research in electric power systems [@Venzke:2019], for problems in probabilistic inference [@Spallitta:2024], +to perform resource analysis on programs [@pham-phd-2024]; +but also for more theoretical and mathematical challenges, like the computation of topological invariants [@co-alenex-2021], and persistent homology [@vm-fods-2022]. + +# Acknowledgements + +We would like to thank the contributors to the `volesti` library for their valuable contributions and feedback. +MP was partially supported by a Cornell University Fellowship, a grant from the A.G. Leventis Foundation, a grant from the Gerondelis Foundation, and a LinkedIn Ph.D. Fellowship. + +# References