Year: 2021
Type: article
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 provides non existing sampling routines to R. We demonstrate the power of volesti by solving several challenging problems using the R language.
Source: The R Journal
DOI: 10.32614/rj-2021-077
Authors: Apostolos Chalkis, Vissarion Fisikopoulos
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
Funder: National Science Foundation
Awards: AF: Small: Collaborative Research: A Computational Theory of Brain Function, TRIPODS+X: RES: Collaborative Research: Scaling Up Descriptive Epidemiology and Metabolic Network Models via Faster Sampling, Collaborative Research: Foundations of Deep Learning: Theory, Robustness, and the Brain, TRIPODS+X: RES: Collaborative Research: Scaling Up Descriptive Epidemiology and Metabolic Network Models via Faster Sampling, AF: Small: Fundamental High-Dimensional Algorithms
OpenAlex ID: W3040140899