Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator

Abstract

We study here a fixed mini-batch gradient descent (FMGD) algorithm to solve optimization problems with massive datasets. In FMGD, the whole sample is split into multiple nonoverlapping partitions. Once the partitions are formed, they are then fixed throughout the rest of the algorithm. For convenience, we refer to the fixed partitions as fixed mini-batches. Then for each computation iteration, the gradients are sequentially calculated on each fixed mini-batch. Because the size of fixed mini-batches is typically much smaller than the whole sample size, it can be easily computed. This leads to much reduced computation cost for each computational iteration. It makes FMGD computationally efficient and practically more feasible. To demonstrate the theoretical properties of FMGD, we start with a linear regression model with a constant learning rate. We study its numerical convergence and statistical efficiency properties. We find that sufficiently small learning rates are necessarily required for both numerical convergence and statistical efficiency. Nevertheless, an extremely small learning rate might lead to painfully slow numerical convergence. To solve the problem, a diminishing learning rate scheduling strategy can be used. This leads to the FMGD estimator with faster numerical convergence and better statistical efficiency. Finally, the FMGD algorithms with random shuffling and a general loss function are also studied. Supplementary materials for this article are available online.

KEYWORDS:

• Fixed mini-batch
• Gradient descent
• Learning rate scheduling
• Random shuffling
• Stochastic gradient descent

Supplementary Materials

The detailed proofs of all propositions and theorems can be found in the supplementary materials.

Acknowledgments

The authors thank the Editor, Associate Editor, and two anonymous reviewers for their careful reading and constructive comments.

Disclosure Statement

The authors report there are no competing interests to declare.

Additional information

Funding

This work is supported by National Natural Science Foundation of China (No.72001205), the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (21XNA026).