Please use this identifier to cite or link to this item:
|Title:||Ordinal optimization with subset selection rule|
|Citation:||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.|
|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|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 5, 2018
WEB OF SCIENCETM
checked on Dec 26, 2018
checked on Dec 22, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.