A new paper proposes that zeroth-order (ZO) optimization algorithms do not necessarily suffer from extra dimension dependencies in their convergence rates compared to first-order (FO) methods. By analyzing optimization algorithms through the lens of dynamical systems and input-to-state stability, the research demonstrates that ZO methods can converge to a neighborhood of FO methods' fixed points. The radius of this neighborhood can be made arbitrarily small by adjusting design parameters, suggesting ZO methods can be made competitive with FO counterparts. AI
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IMPACT This research could lead to more efficient optimization techniques for training machine learning models, potentially reducing computational costs and improving convergence.
RANK_REASON Academic paper on optimization algorithms.