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New complexity bounds established for Moreau--Yosida Langevin sampling algorithm

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new complexity bounds for the Moreau--Yosida unadjusted Langevin algorithm (MYULA), a method used for sampling from probability distributions. The study focuses on distributions of the form $\pi(\,\mathrm{d} x)\propto e^{-f(x)-g(x)}\,\mathrm{d} x$, where $f$ is strongly convex and $g$ is convex and Lipschitz. The findings provide theoretical guarantees on the algorithm's convergence rate, establishing an error bound of $\widetilde O(\varepsilon^{-4/3})$ iterations to achieve a desired precision. AI

IMPACT Establishes theoretical convergence guarantees for a sampling algorithm relevant to machine learning.

RANK_REASON The cluster contains a research paper detailing theoretical advancements in an algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New complexity bounds established for Moreau--Yosida Langevin sampling algorithm

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The cluster contains a research paper detailing theoretical advancements in an algorithm. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yuchen Xin, Zhihua Zhang ·

    Poisson-Corrector Complexity Bounds for Moreau--Yosida Unadjusted Langevin Sampling

    arXiv:2609.12594v1 Announce Type: new Abstract: We study the classical Moreau--Yosida unadjusted Langevin algorithm (MYULA) for $\pi(\,\mathrm{d} x)\propto e^{-f(x)-g(x)}\,\mathrm{d} x$, where $f\in C^2(\mathbb{R}^d)$ is $m$-strongly convex with $L_f$-Lipschitz gradient and $g:\m…

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