The R Journal: article published in 2021, volume 13:2

volesti: Volume Approximation and Sampling for Convex Polytopes in R
Apostolos Chalkis and Vissarion Fisikopoulos , The R Journal (2021) 13:2, pages 642-660.

Abstract Sampling from high-dimensional distributions and volume approximation of convex bodies are fundamental operations that appear in optimization, finance, engineering, artificial intelligence, and machine learning. In this paper, we present volesti, an R package that provides efficient, scalable algorithms for volume estimation, uniform, and Gaussian sampling from convex polytopes. volesti scales to hundreds of dimensions, handles efficiently three different types of polyhedra and pro vides non existing sampling routines to R. We demonstrate the power of volesti by solving several challenging problems using the R language.

Received: 2021-04-12; online 2021-08-17, supplementary material, (3.6 KiB)
CRAN packages: volesti, tmg, multinomineq, lineqGPR, restrictedMVN, tmvmixnorm, hitandrun, limSolve, HybridMC, rhmc, mcmc, MHadaptive, geometry, Rcpp, Rfast, coda, SimplicialCubature, cubature, stats, methods, BH, RcppEigen, testthat, ggplot2, plotly, rgl
CRAN Task Views implied by cited CRAN packages: NumericalMathematics, Bayesian, Multivariate, Distributions, GraphicalModels, HighPerformanceComputing, Optimization, Phylogenetics, SpatioTemporal, TeachingStatistics, WebTechnologies

This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2021-077,
  author = {Apostolos Chalkis and Vissarion Fisikopoulos},
  title = {{volesti: Volume Approximation and Sampling for Convex
          Polytopes in R}},
  year = {2021},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2021-077},
  url = {https://doi.org/10.32614/RJ-2021-077},
  pages = {642--660},
  volume = {13},
  number = {2}
}