Deterministic Zeroth-Order Mirror Descent via Vector Fields with A Posteriori Certification
Masahito Hayashi

TL;DR
This paper introduces a deterministic zeroth-order mirror descent method using vector fields with a posteriori certification, enabling derivative-free optimization with explicit guarantees and broad applicability to information-geometric algorithms.
Contribution
It develops a unified vector-field-driven mirror descent framework with trajectory-wise certification, applicable to derivative-free oracles and broad classes of algorithms.
Findings
Provides explicit last-iterate guarantees under verifiable inequalities.
Instantiates the theory with finite differences requiring 2d+1 evaluations.
Establishes a geometric property linking Bregman identities and conic dominance.
Abstract
We develop a deterministic zeroth-order mirror descent framework by replacing gradients with a general vector field, yielding a vector-field-driven mirror update that preserves Bregman geometry while accommodating derivative-free oracles. Our analysis provides a unified evaluation template for last-iterate function values under a relative-smoothness-type inequality, with an emphasis on trajectory-wise (a posteriori) certification: whenever a verifiable inequality holds along the realized iterates, we obtain explicit last-iterate guarantees. The framework subsumes a broad class of information-geometric algorithms, including generalized Blahut-Arimoto-type updates, by expressing their dynamics through suitable choices of the vector field. We then instantiate the theory with deterministic central finite differences in moderate dimension, where constructing the vector field via…
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Taxonomy
TopicsStochastic Gradient Optimization Techniques · Optimization and Variational Analysis · Advanced Optimization Algorithms Research
