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New blind random search algorithm tackles noisy optimization problems

By PulseAugur Editorial · [1 sources] ·

Researchers have developed an enhanced blind random search algorithm designed to handle optimization problems with noisy measurements. This new method combines a vanishing acceptance threshold with a running average of loss evaluations, proving that it converges almost surely to the global minimizer. The study also analyzes the convergence rate, providing an asymptotic upper bound for low-dimensional cases and a Weibull limiting distribution for high-dimensional scenarios. AI

RANK_REASON Academic paper detailing a new algorithm and its convergence properties. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv stat.ML →

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

New blind random search algorithm tackles noisy optimization problems

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Academic paper detailing a new algorithm and its convergence properties. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Zixian Zhou, James C. Spall ·

    Blind Random Search with Noisy Measurements: Almost Sure Convergence and Convergence Rates

    arXiv:2610.33302v1 Announce Type: cross Abstract: Blind random search is a simple global optimization method that only requires the value of the loss function, without needing gradient information or an explicit loss model. However, its classical formulation assumes that the loss…

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