Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Stochastic approximation type methods for constrained systems: Algorithms and numerical results

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kushner, H.J. ; Brown University, Providence, RI, USA ; Gavin, T.

A stochastic version of the standard nonlinear programming problem is considered. A function f(x) is observed in the presence of noise, and we seek to minimize f(x) for x \in C = {x:q^{i}(x) \leq 0} , where q^{i}(x) are constraints. Numerous practical examples exist. Algorithms are discussed for selecting a sequence Xnwhich converges wp 1 to a point where a necessary condition for optimality holds. The algorithms use, of course, noise-corrupted observations on the f(x) . Numerical results are presented. They indicate that the approach is quite versatile, and can be a useful tool for systematic Monte-Carlo optimization of constrained systems, a much-neglected area. However, many practical problems remain to be resolved, e.g., investigation of efficient one-dimensional search methods and of the tradeoffs between the effort spent per search cycle and the number of search cycles.

Published in:

Automatic Control, IEEE Transactions on  (Volume:19 ,  Issue: 4 )