This indicates that SPSA will converge to the optimal solution within a given level of accuracy with p times fewer measurements of the objective function than the standard method. This is an important practical concern in a wide variety of problems involving Monte Carlo simulations, physical experiments, feedback systems, or incomplete knowledge. Top Some of the general areas for application of SPSA include statistical parameter estimation, simulation-based optimization, pattern recognition, nonlinear regression, signal processing, neural network training, adaptive feedback control, and experimental design. Underground mine detection Wastewater treatment Features of SPSA Top SPSA has several features that make it attractive for many practical applications, such as the ones mentioned above. Because of the efficient gradient approximation, the algorithm is appropriate for high-dimensional problems where many terms are being determined in the optimization process.
Many practical applications have a significant number of terms to be optimized.
SPSA allows for the input to the algorithm to be measurements of the objective function corrupted by noise . For example, this is ideal for the case where Monte Carlo simulations are being used because each simulation run provides one noisy estimate of the performance measure. So, an algorithm explicitly designed to handle noise is needed. Performance guarantees for SPSA exist in the form of an extensive convergence theory . The algorithm has desirable properties for both global and local optimization in the sense that the gradient approximation is sufficiently noisy to allow for escape from local minima while being sufficiently informative about the slope of the function to facilitate local convergence.