Ordinal Optimization with Subset Selection Rule

M.S. Yang and L.H. Lee
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M.S. Yang: Akamai Technologies
L.H. Lee: National University of Singapore

Journal of Optimization Theory and Applications, 2002, vol. 113, issue 3, No 9, 597-620

Abstract: 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.

Keywords: Ordinal optimization; goal softening (search for similar items in EconPapers)
Date: 2002
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DOI: 10.1023/A:1015317022797

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