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Online Optimization of Difference-of-Convex Compositions with Smooth Mappings

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We study online optimization for a broad class of structured non-convex non-smooth problems where each loss is a composition of a difference-of-convex function with a smooth mapping, and the feasible region is defined by constraint functions of the same kind. We propose a time-smoothed proximal linear algorithm and a local-regret measure based on a proximal residual mapping.

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arXiv Machine Learning
Jul 23

Online Optimization of Difference-of-Convex Compositions with Smooth Mappings

arXiv:2607. 19553v1 Announce Type: cross Abstract: We study online optimization for a broad class of structured non-convex non-smooth problems where each loss is a composition of a difference-of-convex function with a smooth mapping, and the feasible region is defined by constraint functions of the same kind.

By Jingwei Ji, Jong-Shi Pang, Renyuan Xu
arXiv Machine Learning
Aug 18

Convex Optimization with Nested Evolving Feasible Sets

arXiv:2605. 07386v2 Announce Type: replace Abstract: \emph{Convex Optimization with Nested Evolving Feasible Sets (CONES)} is considered where the objective function \(f\) remains fixed but the feasible region evolves over time as a nested sequence \(S_1 \supseteq S_2 \supseteq \cdots \supseteq S_T\).

By Karthick Krishna M., Haricharan Balasundaram, Rahul Vaze