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New algorithms achieve linear convergence for unknown smooth regions in optimization

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for optimizing piecewise smooth (PWS) functions when the smooth regions are not known to the algorithm. For convex PWS functions meeting a quadratic growth (QG) condition, a bundle-level (BL)-type method achieves global linear convergence, a first for this problem class. The method is extended to weakly convex PWS problems, matching the complexity of smooth non-convex optimization. Additionally, a verifiable termination criterion is introduced that accurately characterizes the optimality gap without requiring knowledge of problem parameters. AI

IMPACT Introduces novel algorithmic approaches for optimization problems, potentially impacting AI research that relies on efficient function optimization.

RANK_REASON The cluster contains an arXiv preprint detailing new algorithms for optimization problems. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithms achieve linear convergence for unknown smooth regions in optimization

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The cluster contains an arXiv preprint detailing new algorithms for optimization problems. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Zhe Zhang, Suvrit Sra ·

    Linearly Convergent Algorithms for Nonsmooth Problems with Unknown Smooth Regions

    arXiv:2507.19465v2 Announce Type: replace-cross Abstract: We develop efficient algorithms for optimizing piecewise smooth (PWS) functions when their smooth regions are \emph{unknown} to the algorithm. For convex PWS functions satisfying a quadratic growth (QG) condition, we demon…

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