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New algorithm finds stationary points in non-convex functions · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new algorithm for finding stationary points in non-convex functions using a comparison oracle. The algorithm requires approximately \(\\tilde O(n^2/\epsilon^{1.5})\) queries for a function with a Lipschitz gradient and Hessian. Additionally, a quantum algorithm has been proposed that can find an \(\epsilon\)-stationary point with significantly fewer queries, taking \(\tilde O(n/\epsilon^{1.5})\) queries in a quantum comparison oracle model. AI

IMPACT This research could lead to more efficient optimization algorithms for machine learning models.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for finding stationary points in non-convex functions.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithm finds stationary points in non-convex functions · 2 sources tracked

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Helin Wang, Chenyi Zhang, Xiwen Tao, Yexin Zhang, Tongyang Li ·

    Finding Stationary Points by Comparisons

    arXiv:2606.27082v1 Announce Type: new Abstract: We study the problem of finding stationary points of non-convex functions when access to the objective is provided only through a comparison oracle that, given two points, outputs which has the larger function value. For a twice dif…

  2. arXiv cs.LG TIER_1 English(EN) · Tongyang Li ·

    Finding Stationary Points by Comparisons

    We study the problem of finding stationary points of non-convex functions when access to the objective is provided only through a comparison oracle that, given two points, outputs which has the larger function value. For a twice differentiable $f\colon\mathbb R^n\to\mathbb R$ wit…

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