Accelerating Model-Free Optimization via Averaging of Cost Samples

Model-free optimization methods typically rely on cost samples gathered by per- turbing the current solution estimate along a finite and fixed set of directions. However, at each iteration, only the current cost samples are used, while poten- tially informative, previously collected samples are discarded. In this work, we challenge this conventional approach by introducing a simple yet effective memory mechanism that maintains an auxiliary vector of iteratively updated cost samples. By leveraging this stored information, our method estimates descent directions through an averaging of all perturbing directions weighted by the auxiliary vector components. This results in a faster convergence without increasing the number of function queries. By interpreting the resulting algorithm as a time-varying dy- namical system, we are able to establish its convergence properties in the strongly convex case. In particular, by using tools from system theory based on timescale separation, we are able to guarantee a linear convergence rate toward an arbitrarily small neighborhood of the optimal solution. Numerical simulations on regres- sion problems demonstrate that the proposed approach significantly outperforms existing model-free optimization methods