Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis

Atsushi Nitanda, Denny Wu, Taiji Suzuki

Keywords: Neural Network Optimization, Mean field Regime, Overparameterization, Langevin Algorithm

Abstract: We propose the particle dual averaging (PDA) method, which generalizes the dual averaging method in convex optimization to the optimization over probability distributions with quantitative runtime guarantee. The algorithm consists of an inner loop and outer loop: the inner loop utilizes the Langevin algorithm to approximately solve for a stationary distribution, which is then optimized in the outer loop. The method can be interpreted as an extension of the Langevin algorithm to naturally handle nonlinear functional on the probability space. An important application of the proposed method is the optimization of neural network in the mean field regime, which is theoretically attractive due to the presence of nonlinear feature learning, but quantitative convergence rate can be challenging to obtain. By adapting finite-dimensional convex optimization theory into the space of measures, we not only establish global convergence of PDA for two-layer mean field neural networks under more general settings and simpler analysis, but also provide quantitative polynomial runtime guarantee. Our theoretical results are supported by numerical simulations on neural networks with reasonable size.

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TL;DR: Quantitative global convergence rate analysis of two-layer neural networks in the mean field regime for regularized expected/empirical risk minimization.