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Stochastic approximation of constrained systems with system and constraint noise

โœ Scribed by H.J Kushner; E Sanvicente

Publisher
Elsevier Science
Year
1975
Tongue
English
Weight
520 KB
Volume
11
Category
Article
ISSN
0005-1098

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โœฆ Synopsis

Sequential Monte Carlo methods are frequently needed to optzmzze stochastic systems when the performance functwn is observed w~th additive nmse Analysis of convergent algorithms is especially zmportant when there are system parameter constraints.

Sumnmry--The paper considers the problem of mmlmmng f(x) over the constraint set C = {x q~(x)~<0, i m 1, , s}, but the f( ) and q'(.) are unknown and only nokqeperturbed samples of both the f( ) and the q'(.) are available, at selected parameter settings Under certain convexlty condiuons, a stochastic approximation algorithm is set up, and convergence wroth probabdity one to the optimum parameter value is proved Numerous practmcal examples fit the problem descnptmn *

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