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When and Why is Optimistic Multiplicative Weights Slow? The Geometry of Energy Dissipation

NeurIPS 2026 · Optimization › Nonconvex and smooth optimization · approach: Equilibrium learning in zero-sum games · 0 citations (OpenAlex)

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Analyzes last-iterate convergence of optimistic multiplicative weights (OMWU) in zero-sum games via dual energy dissipation, giving tight universal KL rates, uniform lower bounds, and an O~(T^{-1/2}) best-iterate duality gap rate for 2x2 games.

Problem

Understanding when and why the last-iterate convergence of OMWU in two-player zero-sum games can be arbitrarily slow, with sharp quantitative dependence on game-specific constants and uniform rates across distance measures.

Approach

Views OMWU's dual iterates as optimistic skew-gradient descent on the log-sum-exp energy (conjugate of negative entropy); shows energy is dissipative with tight one-step bounds, and that energy change equals KL change in primal space, yielding sharp geometric explanations of slowdown near the simplex boundary.</ask>

Key results

Surprising findings

Limitations

Experimental setup

Metrics: KL divergence total variation distance duality gap convergence rate

Baselines: Wei et al. 2021 OMWU linear rate Lee et al. O(T^{-1/6}) best-iterate rate in duality gap Daskalakis-Panageas OMWU last-iterate

Builds on: OMWU optimistic FTRL Wei et al. 2021 universal last-iterate rate skew-gradient flow

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