BFE and AdaBFE: A New Approach in Learning Rate Automation for Stochastic Optimization

TL;DR

This paper introduces BFE and AdaBFE, new gradient-based methods for automatically adjusting learning rates in stochastic optimization, offering an alternative perspective to existing adaptive and non-adaptive techniques.

Contribution

The paper proposes Binary Forward Exploration (BFE) and Adaptive BFE (AdaBFE), novel gradient-based approaches for automatic learning rate adjustment in stochastic gradient descent.

Findings

Provides a new heuristic approach for learning rate optimization.

Offers an alternative to existing adaptive and non-adaptive methods.

Includes preliminary comparative analysis with traditional methods.

Abstract

In this paper, a new gradient-based optimization approach by automatically adjusting the learning rate is proposed. This approach can be applied to design non-adaptive learning rate and adaptive learning rate. Firstly, I will introduce the non-adaptive learning rate optimization method: Binary Forward Exploration (BFE), and then the corresponding adaptive per-parameter learning rate method: Adaptive BFE (AdaBFE) is possible to be developed. This approach could be an alternative method to optimize the learning rate based on the stochastic gradient descent (SGD) algorithm besides the current non-adaptive learning rate methods e.g. SGD, momentum, Nesterov and the adaptive learning rate methods e.g. AdaGrad, AdaDelta, Adam... The purpose to develop this approach is not to beat the benchmark of other methods but just to provide a different perspective to optimize the gradient descent method, although some comparative study with previous methods will be made in the following sections. This approach is expected to be heuristic or inspire researchers to improve gradient-based optimization combined with previous methods.