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arXiv paper details optimization oracle models and their impact

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This paper reviews models for inexact computational oracles in smooth continuous optimization, focusing on their impact on algorithm convergence, computational complexity, and accuracy. It introduces deterministic and stochastic oracle models, detailing their properties through examples like finite-difference approximations and randomized derivative estimators. The review emphasizes the distinction between absolute and relative errors, bias and variance, and different types of stochastic function evaluations, using common analytical tools to compare convergence and complexity guarantees under various oracle conditions. AI

IMPACT Provides theoretical underpinnings for optimization algorithms used in machine learning and AI research.

RANK_REASON The cluster contains an academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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arXiv paper details optimization oracle models and their impact

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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Albert S. Berahas, Liyuan Cao, Katya Scheinberg ·

    Stochastic and Noisy Oracles in Smooth Continuous Optimization

    arXiv:2610.41086v1 Announce Type: cross Abstract: We review models of inexact computational oracles in smooth continuous optimization and their implications for the convergence, computational complexity, and attainable accuracy of optimization algorithms. We adopt a framework in …

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