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Mathematics > Probability
Title: Taming the Interacting Particle Langevin Algorithm -- the superlinear case
(Submitted on 28 Mar 2024 (v1), last revised 3 Apr 2024 (this version, v2))
Abstract: Recent advances in stochastic optimization have yielded the interactive particle Langevin algorithm (IPLA), which leverages the notion of interacting particle systems (IPS) to efficiently sample from approximate posterior densities. This becomes particularly crucial within the framework of Expectation-Maximization (EM), where the E-step is computationally challenging or even intractable. Although prior research has focused on scenarios involving convex cases with gradients of log densities that grow at most linearly, our work extends this framework to include polynomial growth. Taming techniques are employed to produce an explicit discretization scheme that yields a new class of stable, under such non-linearities, algorithms which are called tamed interactive particle Langevin algorithms (tIPLA). We obtain non-asymptotic convergence error estimates in Wasserstein-2 distance for the new class under an optimal rate.
Submission history
From: Nikos Makras [view email][v1] Thu, 28 Mar 2024 17:11:25 GMT (26kb)
[v2] Wed, 3 Apr 2024 16:24:47 GMT (26kb)
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