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New research paper details YETI method for convex function minimization

By PulseAugur Editorial · [1 sources] ·

A new research paper, "Descending Everest, Part II," explores the complexity of minimizing sums of convex functions with specific smoothness properties. The paper introduces a method called YETI, which combines accelerated proximal and variance reduction techniques. YETI aims to achieve a target accuracy with a bounded number of oracle calls, outperforming first-order methods in experiments on synthetic and Poisson regression problems. AI

RANK_REASON Research paper published on arXiv detailing a new optimization method. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv stat.ML →

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

New research paper details YETI method for convex function minimization

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Research paper published on arXiv detailing a new optimization method. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Mikhail Rudomanov, Ivan Fomin, Alexander V. Gasnikov, Fedor S. Stonyakin ·

    Descending Everest, Part II: Oracle Complexity of Minimizing a Sum of Convex $(L_0,L_1)$-Smooth Functions

    arXiv:2611.04171v2 Announce Type: replace-cross Abstract: We study the minimization of the average of $n$ convex $(L_0,L_1)$-smooth functions: the Hessian norm of each function is bounded by an affine function of its gradient norm. We assume known upper bounds $R$ on the distance…

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