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arXiv · 2601.05056

ZIVR: An Incremental Variance Reduction Technique For Zeroth-Order Composite Problems

Silan Zhang·Yujie Tang
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Abstract

This paper investigates zeroth-order (ZO) finite-sum composite optimization. Recently, variance reduction techniques have been applied to ZO methods to mitigate the non-vanishing variance of 2-point estimators in constrained/composite optimization, yielding improved convergence rates. However, existing ZO variance reduction methods typically involve batch sampling of size at least $\Theta(n)$ or $\Theta(d)$, which can be computationally prohibitive for large-scale problems. In this work, we propose a general variance reduction framework, Zeroth-Order Incremental Variance Reduction (ZIVR), which supports flexible implementations$\unicode{x2014}$including a pure 2-point zeroth-order algorithm that eliminates the need for large batch sampling. Furthermore, we establish comprehensive convergence guarantees for ZIVR across strongly-convex, convex, and non-convex settings that match their first-order counterparts. Numerical experiments validate the effectiveness of our proposed algorithm.

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Silan Zhang, Yujie Tang. 2026-01-08. ZIVR: An Incremental Variance Reduction Technique For Zeroth-Order Composite Problems. https://arxiv.org/abs/2601.05056

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