Title :
A stochastic approximation method for noise-free optimization
Author :
Gerencser, Laszlo ; Vago, Zsuzsanna
Author_Institution :
Comput. & Autom. Inst., Budapest, Hungary
Abstract :
The simultaneous perturbation stochastic approximation or SPSA method for function minimization developed in (Spall, 1999) 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.
Keywords :
approximation theory; minimisation; perturbation techniques; probability; random processes; recursive estimation; stochastic processes; Lyapunov-exponent; SPSA method; estimator sequence; function evaluations; function minimization; geometric rate; noise-free optimization; probability; randomization; simultaneous perturbation stochastic approximation; Approximation methods; Convergence; Europe; Minimization; Optimization; Recursive estimation; Stability analysis; Kiefer-Wolfowitz-methods; Lyapunov-exponents; Optimization; recursive estimation; stochastic approximation;
Conference_Titel :
Control Conference (ECC), 2001 European
Conference_Location :
Porto
Print_ISBN :
978-3-9524173-6-2
