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Non-KKT Accumulation in Entropic Mirror Descent

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arxiv 2608.01658 v1 pith:7PUN7IAW submitted 2026-08-03 math.OC cs.LGmath.DS

classification math.OCcs.LGmath.DS
keywords descentmirroraccumulationboundaryeveryboundedbetagenerated
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abstract

For mirror descent generated by a Legendre kernel, perhaps one of the most basic question in optimization is this: must every accumulation point of a bounded mirror descent sequence be Karush--Kuhn--Tucker (KKT) stationary under proper stepsizes? We show that the answer is no. A longstanding obstacle to resolving this question is the boundary blow-up of the Legendre gradient: it keeps every mirror step in the interior, while at a boundary limit, the inverse entropy metric vanishes on active coordinates and can erase the dual-feasibility in the KKT system. We construct $C^\infty$ objectives and bounded sequences generated by the Shannon-entropic mirror descent on the nonnegative orthant $\R_+^n$, for every $n\geq 3$, and on the probability simplex $\Delta_n$, for every $n\geq 4$, such that, in each case, the set of accumulation points is a smooth boundary circle containing a nonempty relatively open arc of non-KKT points. The steps satisfy $\alpha_k\asymp k^{-\beta}$ with $\beta\in(1/2,1)$, the objective values are nonincreasing, and the objectives are entropy-relatively smooth. Hence the pathology stems from the degeneracy of the Bregman geometry at the boundary, rather than from failure of descent, or improper stepsizes. To the best of our knowledge, these provide the first counterexamples to KKT accumulation for bounded mirror descent sequences with nonincreasing objective values.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Unified Framework for Iterate Convergence of Bregman Proximal Methods

    math.OC 2026-08 conditional novelty 8.0 of 10

    A unified framework using scaled Kurdyka-Lojasiewicz inequalities shows that Bregman proximal point and gradient methods, and mirror flow, converge for closed-domain separable kernels and subanalytic or definable objectives.

  2. Establishing Boundary KKT Convergence of Mirror Descent through Reparameterization

    math.OC 2026-08 conditional novelty 7.0 of 10

    Under verifiable joint conditions on the objective, the Legendre kernel, and the feasible geometry, mirror descent converges to a boundary KKT point with explicit rates.

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