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New algorithms offer improved convergence for mean-field min-max problems

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Researchers have developed two variants of the mirror descent-ascent (MDA) algorithm to address min-max problems within the space of measures. The study establishes non-asymptotic convergence rates for both simultaneous and alternating MDA, with the alternating version showing improved performance. A key technical contribution involves an infinite-dimensional dual space analysis that connects Bregman divergences on measures to those on bounded continuous functions, enabling better control over alternating update terms. AI

RANK_REASON This is a research paper detailing a new algorithmic approach to optimization problems. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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

New algorithms offer improved convergence for mean-field min-max problems

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This is a research paper detailing a new algorithmic approach to optimization problems. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Razvan-Andrei Lascu, Mateusz B. Majka, {\L}ukasz Szpruch ·

    Mirror Descent-Ascent for mean-field min-max problems

    arXiv:2402.08106v3 Announce Type: replace-cross Abstract: We study two variants of the mirror descent-ascent (MDA) algorithm for solving min-max problems on the space of measures: simultaneous and alternating. We work under assumptions of convexity-concavity and relative smoothne…

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