Online Optimization of Difference-of-Convex Compositions with Smooth Mappings
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. We show that this residual is a proper stationarity measure for the original problem: its fixed-point condition implies first-order stationarity. Our analysis relies on a tangent-cone characterization for a feasible region described by composite difference-of-convex constraints, which is of independent interest and al...
| Authors | Jingwei Ji, Jong-Shi Pang, Renyuan Xu |
|---|---|
| Primary category | math.OC |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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