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.
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
NumericalMathematics, Bayesian, Multivariate, Distributions, GraphicalModels, HighPerformanceComputing, Optimization, Phylogenetics, SpatioTemporal, TeachingStatistics, WebTechnologies
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For attribution, please cite this work as
Chalkis & Fisikopoulos, "volesti: Volume Approximation and Sampling for Convex Polytopes in R", The R Journal, 2021
BibTeX citation
@article{RJ-2021-077,
author = {Chalkis, Apostolos and Fisikopoulos, Vissarion},
title = {volesti: Volume Approximation and Sampling for Convex Polytopes in R},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-077},
doi = {10.32614/RJ-2021-077},
volume = {13},
issue = {2},
issn = {2073-4859},
pages = {642-660}
}