论文标题
volesti：r re的体积近似和凸多属的采样
Volesti: Volume Approximation and Sampling for Convex Polytopes in R
论文作者
论文摘要
来自高维分布的采样和凸体的体积近似是出现在优化，金融，工程，人工智能和机器学习中的基本操作。在本文中，我们提出了Volesti，Revesti是一个R包，可提供有效的，可扩展的算法，以供体积估计，均匀的凸多属构图，均匀和高斯采样。 Volesti缩放到数百个维度，有效地处理三种不同类型的Polyhedra，并为R提供了非现有的抽样例程。我们通过使用R语言解决了几个具有挑战性的问题来证明Volesti的力量。
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.
