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New algorithm improves convergence rates for variational inequalities

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new stochastic algorithm incorporating Halpern anchoring to address constrained convex-concave problems and monotone variational inequalities. This single-loop, single-call algorithm utilizes an unbiased sample of the gradient operator at each iteration, making it suitable for monotone games with noisy feedback. The algorithm achieves an anytime last-iterate convergence rate of O(t^{-1/4}) for both gradient-mapping norm and restricted gap, surpassing the previous best rate of O(t^{-1/5}) for the restricted gap. AI

IMPACT This research advances optimization techniques relevant to machine learning and game theory.

RANK_REASON The cluster contains a research paper detailing a new algorithm and its theoretical guarantees. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

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New algorithm improves convergence rates for variational inequalities

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The cluster contains a research paper detailing a new algorithm and its theoretical guarantees. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jun-Hyun Kim, Ahmet Alacaoglu ·

    Improving the Last-Iterate Guarantees of Anytime Algorithms for Stochastic Monotone Variational Inequalities

    arXiv:2609.15257v1 Announce Type: cross Abstract: We analyze a stochastic algorithm with Halpern anchoring for constrained convex-concave problems and monotone variational inequalities. This algorithm is single-loop and single-call since it uses one unbiased sample of the gradien…

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