volume 9, issue 5, P580 2021
DOI: 10.3390/math9050580
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Pavel Shcherbakov, Mingyue Ding, Ming Yuchi

Abstract: Various Monte Carlo techniques for random point generation over sets of interest are widely used in many areas of computational mathematics, optimization, data processing, etc. Whereas for regularly shaped sets such sampling is immediate to arrange, for nontrivial, implicitly specified domains these techniques are not easy to implement. We consider the so-called Hit-and-Run algorithm, a representative of the class of Markov chain Monte Carlo methods, which became popular in recent years. To perform random samp…

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