Title :
SPSA in noise free optimization
Author :
Gerencsér, László ; Vágó, Zsuzsanna
Author_Institution :
Comput. & Autom. Inst., Hungarian Acad. of Sci., Budapest, Hungary
Abstract :
The SPSA (simultaneous perturbation stochastic approximation) method for function minimization developed in Spall (1992) is analyzed for optimization problems without measurement noise. We prove the result that under appropriate technical conditions the estimator sequence converges to the optimum with geometric rate with probability 1. Numerical experiments support the conjecture that the top Lyapunov-exponent defined in terms of the SPSA method is smaller than the Lyapunov-exponent of its deterministic counterpart. We conclude that randomization improves convergence rate while dramatically reducing the number of function evaluations
Keywords :
approximation theory; convergence; optimisation; probability; recursive estimation; convergence rate; estimator sequence; function minimization; noise free optimization; randomization; simultaneous perturbation stochastic approximation; top Lyapunov-exponent; Automation; Convergence; Estimation error; Measurement errors; Minimization methods; Noise measurement; Optimization methods; Recursive estimation; Stochastic processes; Stochastic resonance;
Conference_Titel :
American Control Conference, 2000. Proceedings of the 2000
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-5519-9
DOI :
10.1109/ACC.2000.879172
