Regret in polyhedral online convex optimization equals Θ(√((1+RS_T) T log V_max)) where RS_T counts active region switches.
Mathematics of Operations Research42(1), 95–118 (2017)
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
RA-DCA applies randomized vertex screening inside DCA iterations for max-structured DC programs and proves that safeguarded accumulation points are directionally stationary with probability one under regularity, active-set consistency, and random-embedding assumptions.
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Polyhedral Instability Governs Regret in Online Learning
Regret in polyhedral online convex optimization equals Θ(√((1+RS_T) T log V_max)) where RS_T counts active region switches.
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RA-DCA: A Randomized Active-Set DCA for Directional Stationarity in Max-Structured DC Programs
RA-DCA applies randomized vertex screening inside DCA iterations for max-structured DC programs and proves that safeguarded accumulation points are directionally stationary with probability one under regularity, active-set consistency, and random-embedding assumptions.