An Adaptive Variance Reduction Zeroth-Order Algorithm for Finite-Sum Optimization

Abstract

The unconstrained finite-sum optimization problem is a common type of problem in the field of optimization, and there is currently limited research on zeroth-order optimization algorithms. To solve unconstrained finite-sum optimization problems for non-convex function, we propose a zeroth-order optimization algorithm with adaptive variance reduction, called ZO-AdaSPIDER for short. Then, we analyze the convergence performance of the algorithm. The theoretical results show that ZO-AdaSPIDER algorithm can converge to -stationary point when facing non-convex function, and its convergence rate is .