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In this paper, a new gradient-based optimization approach by automaticallyadjusting the learning rate is proposed. This approach can be applied to designnon-adaptive learning rate and adaptive learning rate. Firstly, I willintroduce the non-adaptive learning rate optimization method: Binary ForwardExploration (BFE), and then the corresponding adaptive per-parameter learningrate method: Adaptive BFE (AdaBFE) is possible to be developed. This approachcould be an alternative method to optimize the learning rate based on thestochastic gradient descent (SGD) algorithm besides the current non-adaptivelearning rate methods e.g. SGD, momentum, Nesterov and the adaptive learningrate methods e.g. AdaGrad, AdaDelta, Adam... The purpose to develop thisapproach is not to beat the benchmark of other methods but just to provide adifferent perspective to optimize the gradient descent method, although somecomparative study with previous methods will be made in the following sections.This approach is expected to be heuristic or inspire researchers to improvegradient-based optimization combined with previous methods.

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