Ordinal optimization with subset selection rule

Authors:
M. S. Yang;L. H. Lee
Affiliations:
Manager, Akamai Technologies, Cambridge, Massachusetts;Assistant Professor, Department of Industrial and Systems Engineering, National University of Singapore, Kent Ridge, Singapore
Venue:
Journal of Optimization Theory and Applications
Year:
2002

Citing 2
Cited 0

Convergence properties of ordinal comparison in the simulation of discrete event dynamic systems

Journal of Optimization Theory and Applications
Universal alignment probabilities and subset selection for ordinal optimization

Journal of Optimization Theory and Applications

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Abstract

Ordinal optimization (OO) has enjoyed a great degree of success in addressing stochastic optimization problems characterized by an independent and identically distributed (i.i.d.) noise. The methodology offers a statistically quantifiable avenue to find good enough solutions by means of soft computation. In this paper, we extend the OO methodology to a more general class of stochastic problems by relaxing the i.i.d, assumption on the underlying noise. Theoretical results and their applications to simple examples are presented.