Improved Binary Forward Exploration: Learning Rate Scheduling Method for Stochastic Optimization
This work provides an incremental improvement to gradient-based optimization methods, offering a different perspective for researchers and practitioners in machine learning.
The paper tackles the problem of learning rate scheduling in stochastic optimization by proposing an improved version of Binary Forward Exploration (BFE), which combines first-order and second-order optimization advantages for speed and efficiency, but does not aim to outperform existing methods like SGD with momentum or Adam.
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.