Exploring the truth and beauty of theory landscapes with machine learning
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Astronomy & Astrophysics↗
Engineering topics
Matchev, Konstantin T.
Publications and source records attributed to Matchev, Konstantin T..
Variance reduction via simultaneous importance sampling and control variates techniques using vegas
Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Codebase release r1.4 for CoVVVR
Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named COVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.
Shyamsundar, Prasanth↗