Particle exchange Monte Carlo methods for eigenfunction and related nonlinear problems

Fuente: arXiv
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Main Authors: Dupuis, Paul, Zhang, Benjamin J.
Format: Preprint
Published: 2025
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author Dupuis, Paul
Zhang, Benjamin J.
author_facet Dupuis, Paul
Zhang, Benjamin J.
contents We introduce and develop a novel particle exchange Monte Carlo method. Whereas existing methods apply to eigenfunction problems where the eigenvalue is known (e.g., integrals with respect to a Gibbs measure, which can be interpreted as corresponding to eigenvalue zero), here the focus is on problems where the eigenvalue is not known a priori. To obtain an appropriate particle exchange rule we must consider a pair of processes, with one evolving forward in time and the other backward. Applications to eigenfunction problems corresponding to quasistationary distributions and ergodic stochastic control are discussed.
format Preprint
id arxiv_https___arxiv_org_abs_2505_23456
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Particle exchange Monte Carlo methods for eigenfunction and related nonlinear problems
Dupuis, Paul
Zhang, Benjamin J.
Numerical Analysis
Computation
We introduce and develop a novel particle exchange Monte Carlo method. Whereas existing methods apply to eigenfunction problems where the eigenvalue is known (e.g., integrals with respect to a Gibbs measure, which can be interpreted as corresponding to eigenvalue zero), here the focus is on problems where the eigenvalue is not known a priori. To obtain an appropriate particle exchange rule we must consider a pair of processes, with one evolving forward in time and the other backward. Applications to eigenfunction problems corresponding to quasistationary distributions and ergodic stochastic control are discussed.
title Particle exchange Monte Carlo methods for eigenfunction and related nonlinear problems
topic Numerical Analysis
Computation
url https://arxiv.org/abs/2505.23456