aiqbench analysis
This paper advances optimization theory by establishing rigorous convergence certificates for mirror descent algorithms using deterministic finite differences, a technique relevant to zeroth-order gradient estimation in complex AI landscapes. By deriving explicit scaling laws for central-difference vectors under uniform Hessian bounds, the work provides theoretical guarantees that could inform more robust training dynamics and hyperparameter selection strategies.
Trajectory-Wise Certification for Vector-Field Mirror Descent with Deterministic Finite Differences
arXiv:2602.00634v2 Announce Type: replace-cross Abstract: We study mirror descent in which the objective gradient is replaced by a general vector field. Since such a field does not automatically relate the mirror update to the objective gap, we introduce a trajectory-wise generalized relative-smoothness condition and a generalized star-convexity interface. Together they yield a finite-horizon, a posteriori last-iterate certificate with an accumulated-stepsize term and an exceptional-region-dependent error term. We also give a pointwise sufficient condition for positive admissible stepsizes, displaying the effects of objective smoothness, mirror-map conditioning, and vector-field mismatch. We then construct a deterministic zeroth-order instance from coordinate central differences. The function values place the unknown gradient in an explicit uncertainty box, and verification of the interface reduces to robust conic dominance. We derive an explicit scaling of the central-difference vector that guarantees the required dominance over the gradient uncertainty set. Under uniform Hessian bounds, the resulting vector field satisfies the interface outside an explicit resolution-dependent neighborhood. The final guarantee combines a rate term governed by accumulated accepted stepsizes with a finite-resolution error floor.
Abstract:We study mirror descent in which the objective gradient is replaced by a general vector field. Since such a field does not automatically relate the mirror update to the objective gap, we introduce a trajectory-wise generalized relative-smoothness condition and a generalized star-convexity interface. Together they yield a finite-horizon, a posteriori last-iterate certificate with an accumulated-stepsize term and an exceptional-region-dependent error term. We also give a pointwise sufficient condition for positive admissible stepsizes, displaying the effects of objective smoothness, mirror-map conditioning, and vector-field mismatch. We then construct a deterministic zeroth-order instance from coordinate central differences. The function values place the unknown gradient in an explicit uncertainty box, and verification of the interface reduces to robust conic dominance. We derive an explicit scaling of the central-difference vector that guarantees the required dominance over the gradient uncertainty set. Under uniform Hessian bounds, the resulting vector field satisfies the interface outside an explicit resolution-dependent neighborhood. The final guarantee combines a rate term governed by accumulated accepted stepsizes with a finite-resolution error floor.
Optimization and Control (math.OC); Information Theory (cs.IT); Quantum Physics (quant-ph)
arXiv:2602.00634 [math.OC]
(or arXiv:2602.00634v2 [math.OC] for this version)
https://doi.org/10.48550/arXiv.2602.00634 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 267 words
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