SA-GD: Improved Gradient Descent Learning Strategy with Simulated Annealing

Zhicheng Cai

Gradient descent algorithm is the most utilized method when optimizing machine learning issues. However, there exists many local minimums and saddle points in the loss function, especially for high dimensional non-convex optimization problems like deep learning. Gradient descent may make loss function trapped in these local intervals which impedes further optimization, resulting in poor generalization ability. This paper proposes the SA-GD algorithm which introduces the thought of simulated annealing algorithm to gradient descent. SA-GD method offers model the ability of mounting hills in probability, tending to enable the model to jump out of these local areas and converge to a optimal state finally. We took CNN models as an example and tested the basic CNN models on various benchmark datasets. Compared to the baseline models with traditional gradient descent algorithm, models with SA-GD algorithm possess better generalization ability without sacrificing the efficiency and stability of model convergence. In addition, SA-GD can be utilized as an effective ensemble learning approach which improves the final performance significantly.

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