Stochastic Regularized Majorization-Minimization with Weakly Convex and Multi-Convex Surrogates

Abstract

Stochastic majorization-minimization (SMM) is a class of stochastic optimization algorithms that proceed by sampling new data points and minimizing a recursive average of surrogate functions of an objective function. The surrogates are required to be strongly convex and the existing convergence rate analysis for the general non-convex setting was not available. In this paper, we propose an extension of SMM where surrogates are allowed to be only weakly convex or block multi-convex, and the averaged surrogates are approximately minimized with proximal regularization or block-minimized within diminishing radii, respectively. For the general nonconvex constrained setting with non-i.i.d. data samples, we show that the first-order optimality gap of the proposed algorithm decays at the rate $\widetilde{O}(n^{-1/4})$ for the empirical loss and $\widetilde{O}(n^{-1/8})$ for the expected loss, where $n$ denotes the number of data samples processed. Under some additional assumption, the latter convergence rate can be improved to $\widetilde{O}(n^{-1/4})$. As a corollary, we obtain the first convergence rate bounds for various optimization methods under general nonconvex non-i.i.d. data setting: Double-averaging projected gradient descent and its generalizations, proximal point empirical risk minimization, and online matrix/tensor decomposition algorithms. We also provide experimental validation of our results.