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New algorithm tackles complex non-convex optimization problems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new algorithm for online optimization of complex non-convex problems. The proposed Proximal Linear Algorithm, based on a time-smoothed approach and a proximal residual mapping, offers a method to achieve first-order stationarity. This algorithm's analysis utilizes a tangent-cone characterization for feasible regions defined by composite difference-of-convex constraints, enabling updates via a convex optimization oracle. The work also establishes bounds on local regret and the number of inner convex subproblems, alongside an error bound for approximate stationarity. AI

RANK_REASON The cluster contains a research paper published on arXiv detailing a new algorithm for optimization problems. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New algorithm tackles complex non-convex optimization problems

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The cluster contains a research paper published on arXiv detailing a new algorithm for optimization problems. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jingwei Ji, Jong-Shi Pang, Renyuan Xu ·

    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 constrain…

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