DocumentCode :
3539144
Title :
Approximate IPA: Trading unbiasedness for simplicity
Author :
Wardi, Y. ; Cassandras, Christos
Author_Institution :
Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
fYear :
2013
fDate :
10-13 Dec. 2013
Firstpage :
7603
Lastpage :
7608
Abstract :
When Perturbation Analysis (PA) yields unbiased sensitivity estimators for expected-value performance functions in discrete event dynamic systems, it can be used for performance optimization of those functions. However, when PA is known to be unbiased, the complexity of its estimators often does not scale with the system´s size. The purpose of this paper is to suggest an alternative approach to optimization which balances precision with computing efforts by trading off complicated, unbiased PA estimators for simple, biased approximate estimators. Furthermore, we provide guidelines for developing such estimators, that are largely based on the Stochastic Flow Modeling framework. We suggest that if the relative error (or bias) is not too large, then optimization algorithms such as stochastic approximation converge to a (local) minimum just like in the case where no approximation is used. We apply this approach to an example of balancing loss with buffer-cost in a finite-buffer queue, and prove a crucial upper bound on the relative error. This paper presents the initial study of the proposed approach, and we believe that if the idea gains traction then it may lead to a significant expansion of the scope of PA in optimization of discrete event systems.
Keywords :
approximation theory; discrete event systems; optimisation; perturbation techniques; stochastic processes; approximate IPA; biased approximate estimators; buffer-cost; discrete event dynamic systems; expected-value performance functions; finite-buffer queue; optimization algorithms; performance optimization; perturbation analysis; stochastic approximation convergence; stochastic flow modeling framework; trading unbiasedness; unbiased sensitivity estimators; Approximation algorithms; Approximation methods; Computational modeling; Equations; Mathematical model; Optimization; Trajectory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on
Conference_Location :
Firenze
ISSN :
0743-1546
Print_ISBN :
978-1-4673-5714-2
Type :
conf
DOI :
10.1109/CDC.2013.6761096
Filename :
6761096
Link To Document :
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