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Author (up) Catumba, G.; Ramos, A.; Zaldivar, B.
Title Stochastic automatic differentiation for Monte Carlo processes Type Journal Article
Year 2025 Publication Computer Physics Communications Abbreviated Journal Comput. Phys. Commun.
Volume 307 Issue Pages 109396 - 13pp
Keywords Automatic differentiation; Monte Carlo; Lattice field theory; Optimization; Bayesian inference
Abstract Monte Carlo methods represent a cornerstone of computer science. They allow sampling high dimensional distribution functions in an efficient way. In this paper we consider the extension of Automatic Differentiation (AD) techniques to Monte Carlo processes, addressing the problem of obtaining derivatives (and in general, the Taylor series) of expectation values. Borrowing ideas from the lattice field theory community, we examine two approaches. One is based on reweighting while the other represents an extension of the Hamiltonian approach typically used by the Hybrid Monte Carlo (HMC) and similar algorithms. We show that the Hamiltonian approach can be understood as a change of variables of the reweighting approach, resulting in much reduced variances of the coefficients of the Taylor series. This work opens the door to finding other variance reduction techniques for derivatives of expectation values.
Address [Catumba, Guilherme; Ramos, Alberto; Zaldivar, Bryan] UVEG, CSIC, IFIC, Edificio Inst Invest,Apt 22085, E-46071 Valencia, Spain, Email: gtelo@ific.uv.es
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0010-4655 ISBN Medium
Area Expedition Conference
Notes WOS:001337289000001 Approved no
Is ISI yes International Collaboration no
Call Number IFIC @ pastor @ Serial 6297
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