Develops a constrained optimization framework for position-independent irreversible perturbations of ULA that yields an explicit optimal design accounting for both spectral-gap mixing and weighted jump-distance bias.
Irreversible langevin samplers and variance reduction: a large deviations approach.Nonlinearity, 28(7):2081–2103, May 2015a
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Optimizing Irreversible Perturbations of the Unadjusted Langevin Algorithm
Develops a constrained optimization framework for position-independent irreversible perturbations of ULA that yields an explicit optimal design accounting for both spectral-gap mixing and weighted jump-distance bias.