Variance reduction via simultaneous importance sampling and control variates techniques using vegas

Shyamsundar, Prasanth; Scott, Jacob L.; Mrenna, Stephen; Matchev, Konstantin T.; Kong, Kyoungchul

doi:10.21468/SciPostPhysCodeb.28

Title: Variance reduction via simultaneous importance sampling and control variates techniques using vegas

Journal Article · 04 March 2024 · SciPost Physics Codebases

DOI: https://doi.org/10.21468/SciPostPhysCodeb.28 · OSTI ID:2318588

Shyamsundar, Prasanth; Scott, Jacob L.; Mrenna, Stephen; Matchev, Konstantin T.; Kong, Kyoungchul

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.

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Sponsoring Organization:: USDOE

Grant/Contract Number:: DEAC02-07CH11359; 0000240323; SC0019219; SC0022148; SC0024407; SC0024673

OSTI ID:: 2318588

Journal Information:: SciPost Physics Codebases, Journal Name: SciPost Physics Codebases; ISSN 2949-804X

Publisher:: Stichting SciPostCopyright Statement

Country of Publication:: Netherlands

Language:: English

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