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New algorithm closes optimization gap for derivative-free problems

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new algorithm for derivative-free stochastic convex optimization in one dimension. This algorithm achieves an optimal convergence rate of O(1/sqrt(T)), closing a persistent gap between theoretical upper bounds and lower bounds in this specific area of optimization. The work provides the first sharp rate guarantee for this type of problem, which involves minimizing a convex function using noisy function evaluations without direct gradient information. AI

IMPACT This research advances theoretical understanding in optimization, potentially impacting AI model training and other computationally intensive tasks that rely on efficient optimization techniques.

RANK_REASON The cluster contains an academic paper published on arXiv detailing a new algorithm for a specific optimization problem.

Read on arXiv stat.ML →

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

New algorithm closes optimization gap for derivative-free problems

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The cluster contains an academic paper published on arXiv detailing a new algorithm for a specific optimization problem.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Alexandra Carpentier, Chlo\'e Rouyer, Alexandre Tsybakov, Arya Akhavan ·

    Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension

    arXiv:2607.12938v1 Announce Type: cross Abstract: Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evaluations, a logarithmic gap has persisted between kn…

  2. arXiv stat.ML TIER_1 English(EN) · Arya Akhavan ·

    Sharp Optimal Algorithm for Derivative-Free Stochastic Convex Optimization in One Dimension

    Stochastic convex optimization is a classical problem with well-understood guarantees under first-order feedback. In contrast, for zero-order optimization with noisy function evaluations, a logarithmic gap has persisted between known upper bounds and the $Ω(1/\sqrt{T})$ lower bou…

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