Improved Binary Forward Exploration: Learning Rate Scheduling Method for Stochastic Optimization

TL;DR

This paper investigates improved algorithms for Binary Forward Exploration, a gradient-based learning rate scheduling method, aiming to enhance efficiency and robustness in stochastic optimization by combining first- and second-order optimization advantages.

Contribution

It introduces improved algorithms for BFE, offering a new perspective on learning rate scheduling that balances speed and robustness in stochastic optimization.

Findings

Compared with SGD, Adam, and momentum methods, the improved BFE algorithms show enhanced efficiency.

The approach provides a novel viewpoint on optimizing gradient descent processes.

Experimental results demonstrate robustness and efficiency improvements.

Abstract

A new gradient-based optimization approach by automatically scheduling the learning rate has been proposed recently, which is called Binary Forward Exploration (BFE). The Adaptive version of BFE has also been discussed thereafter. In this paper, the improved algorithms based on them will be investigated, in order to optimize the efficiency and robustness of the new methodology. This improved approach provides a new perspective to scheduling the update of learning rate and will be compared with the stochastic gradient descent, aka SGD algorithm with momentum or Nesterov momentum and the most successful adaptive learning rate algorithm e.g. Adam. The goal of this method does not aim to beat others but provide a different viewpoint to optimize the gradient descent process. This approach combines the advantages of the first-order and second-order optimizations in the aspects of speed and efficiency.