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]
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