Simultaneous Perturbation Stochastic Approximation

Simultaneous Perturbation Stochastic Approximation (SPSA) is a gradient-free optimization method used to efficiently estimate gradients in high-dimensional spaces, particularly useful when evaluating the objective function is computationally expensive. Current research focuses on improving SPSA's accuracy and stability, including hybrid approaches that combine SPSA with other gradient estimation techniques (e.g., parameter-shift rules) and developing decentralized, low-bandwidth versions for distributed computing environments. These advancements are significant for applications like training large language models and optimizing variational quantum algorithms, where reducing computational cost and communication overhead is crucial.