Parallel Architecture Enhances Adaptivity in Stochastic Gradient Methods

The research proposes a novel parallel computing framework designed to enhance the adaptivity of stochastic gradient methods. This architecture leverages multiple processors, each executing a static gradient descent algorithm with varying parameters. By systematically exploring different iteration counts in a geometric sequence, the framework aims to identify the most suitable parameters for achieving desired convergence conditions. This approach allows for a more dynamic and responsive optimization process compared to traditional static methods. The method is described as assembling static gradient methods to achieve better adaptivity, with each processor executing a gradient descent with a step size chosen as a predetermined function of the number of iterations.