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|Title:||Ordinal optimization with subset selection rule|
|Source:||Yang, M.S., Lee, L.H. (2002-06). Ordinal optimization with subset selection rule. Journal of Optimization Theory and Applications 113 (3) : 597-620. ScholarBank@NUS Repository. https://doi.org/10.1023/A:1015317022797|
|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.|
|Source Title:||Journal of Optimization Theory and Applications|
|Appears in Collections:||Staff Publications|
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