Updating algorithms with multi-step stochastic correction

Updating algorithms with multi-step stochastic correction

Nested multi-step stochastic correction offers a possibility to improve updating algorithms for numerical simulations of lattice gauge theories with fermions. The corresponding generalisations of the two-step multi-boson (TSMB) algorithm as well as some applications with hybrid Monte Carlo (HMC) algorithms are considered.

Multi-Step stochastic correction in dynamical fermion updating algorithms

The advantages of using Multi-Step corrections for simulations of lattice gauge theories with dynamical fermions will be discussed. This technique is suited for algorithms based on the MultiBoson representation of the dynamical fermions as well as for the Hybrid Monte-Carlo (HMC) algorithm and variants of the latter, like the Polynomial-HMC. Especially the latter has the power to deal with an o...

A first-order stochastic primal-dual algorithm with correction step

We investigate the convergence properties of a stochastic primal-dual splitting algorithm for solving structured monotone inclusions involving the sum of a cocoercive operator and a composite monotone operator. The proposed method is the stochastic extension to monotone inclusions of a proximal method studied in [26, 35] for saddle point problems. It consists in a forward step determined by the...

Multi-level stochastic approximation algorithms

Mean-square Convergence of Stochastic Multi-step Methods with Variable Step-size

Abstract. We study mean-square consistency, stability in the mean-square sense and meansquare convergence of drift-implicit linear multi-step methods with variable step-size for the approximation of the solution of Itô stochastic differential equations. We obtain conditions that depend on the step-size ratios and that ensure mean-square convergence for the special case of adaptive two-step Maru...