SA-PAL combines adaptive timesteps and position-dependent friction in Langevin dynamics, reporting 1.5-3x faster mixing on Rosenbrock and Mueller-Brown potentials plus order-of-magnitude efficiency gains on other test problems.
Monte Carlo Methods in Statistical Mechanics: Foundations and New Algorithms, pp.\ 131--192
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New discrete-time approximations to SG(L)D enable accurate non-asymptotic predictions of covariance and integrated autocorrelation time for practical tuning in large-batch or misspecified regimes.
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Accelerated sampling using SamAdams variable timesteps and position-adaptive Langevin dynamics
SA-PAL combines adaptive timesteps and position-dependent friction in Langevin dynamics, reporting 1.5-3x faster mixing on Rosenbrock and Mueller-Brown potentials plus order-of-magnitude efficiency gains on other test problems.