Experimental Results for Gradient Estimation and Optimization of a Markov Chain in Steady-State

Infinitesimal perturbation analysis (IPA) and the likelihood ratio (LR) method have drawn tots of attention recently, as ways of estimating the gradient of a performance measure with respect to continuous parameters in dynamic stochastic systems. In this paper, we experiment with the use of these estimators in stochastic approximation algorithms, to perform so-called “single-run optimizations” of steady-state systems, as suggested in [23]. We also compare them to finite-difference estimators, with and without common random numbers. In most cases, the simulation length must be increased from iteration to iteration, otherwise the algorithm converges to the wrong value. We have performed extensive numerical experiments with a simple M/M/1 queue. We state convergence results, but do not give the proofs. The proofs are given in [14].