Accelerated Simultaneous Perturbation Stochastic Approximation for Tracking Under Unknown-but-Bounded Disturbances

Victoria Erofeeva, Oleg N. Granichin, Munira Tursunova, Anna Sergeenko, Yuming Jiang

Abstract: In this paper, we propose an accelerated version of Simultaneous Perturbation Stochastic Approximation (Accelerated SPSA). This algorithm belongs to the class of methods used in derivative-free optimization and has proven efficacy in the problems including significant non-statistical uncertainties. We focus on analysis of Accelerated SPSA in a non-stationary setting and consider the presence of unknown-but-bounded disturbances. Research on these problems covers many directions. However, in large-scale systems, efficiency still remains a concern. It gave rise to the research where acceleration represents an objective in the algorithm’s design. This problem motivated us to extend our previous research on SPSA in the direction of acceleration. We show that the proposed new accelerated version converges faster than the initial one. The validation of the algorithm is preformed in a target tracking problem.