Abstract
The simultaneous perturbation stochastic approximation or SPSA method for function minimization developed in [15] is analyzed for optimization problems without measurement noise. We prove that, under appropriate technical conditions, the estimator sequence converges to the optimum with geometric rate with probability 1. Numerical experiments are carried out to determine the top Lyapunov-exponent. We conclude that randomization improves convergence rate while significantly reducing the number of function evaluations.
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