Block Coordinate Descent for Neural Networks Provably Finds Global Minima

Shunta Akiyama

Keywords: Deep learning theory, block coordinate descent, global convergence, generalization error analysis

TL;DR: This paper proves that a block coordinate descent algorithm can train deep neural networks to global minima under certain activation functions, extending to ReLU via architectural modifications.

Abstract: In this paper, we consider a block coordinate descent (BCD) algorithm for training deep neural networks and provide a new global convergence guarantee under strictly monotonically increasing activation functions. While existing works demonstrate convergence to stationary points for BCD in neural networks, our contribution is the first to prove convergence to global minima, ensuring arbitrarily small loss. We show that the loss with respect to the output layer decreases exponentially while the loss with respect to the hidden layers remains well-controlled. Additionally, we derive generalization bounds using the Rademacher complexity framework, demonstrating that BCD not only achieves strong optimization guarantees but also provides favorable generalization performance. Moreover, we propose a modified BCD algorithm with skip connections and non-negative projection, extending our convergence guarantees to ReLU activation, which are not strictly monotonic. Empirical experiments confirm our theoretical findings, showing that the BCD algorithm achieves a small loss for strictly monotonic and ReLU activations.

Primary Area: Theory (e.g., control theory, learning theory, algorithmic game theory)

Submission Number: 19787