Non-KKT Accumulation in Entropic Mirror Descent
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Abstract (arXiv)
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^∞ objectives and bounded sequences generated by the Shannon-entropic mirror descent on the nonnegative orthant R_+n, for every n≥ 3, and on the probability simplex Deltan, for every n≥ 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 αkasymp k-β with β∈(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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 3 Aug 2026.
Builds on
17 of 36 references.
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5 more
- 2023Nonconvex Stochastic Bregman Proximal Gradient Method with Application to Deep Learningcited by 7
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Citing papers per year: 2026: 1.
1 paper.