The R Journal: accepted article

This article will be copy edited and may be changed before publication.

volesti: Volume Approximation and Sampling for Convex Polytopes in R PDF download
Apostolos Chalkis and Vissarion Fisikopoulos

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 algo rithms for volume estimation, uniform and Gaussian sampling from convex polytopes. volesti scales to hundreds of dimensions, handles efficiently three different types of polyhedra and provides 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 Kb)
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, Graphics, Multivariate, Distributions, gR, HighPerformanceComputing, Optimization, Phylogenetics, SpatioTemporal, TeachingStatistics, WebTechnologies


CC BY 4.0
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://journal.r-project.org/archive/2021/RJ-2021-077/index.html}
}