1. ScholarBank@NUS
  2. 1. Staff
  3. Staff Publications
Please use this identifier to cite or link to this item: https://doi.org/10.1023/A:1015317022797
DC FieldValue
dc.titleOrdinal optimization with subset selection rule
dc.contributor.authorYang, M.S.
dc.contributor.authorLee, L.H.
dc.date.accessioned2014-10-07T10:24:53Z
dc.date.available2014-10-07T10:24:53Z
dc.date.issued2002-06
dc.identifier.citationYang, 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
dc.identifier.issn00223239
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/87156
dc.description.abstractOrdinal 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1023/A:1015317022797
dc.sourceScopus
dc.subjectgoal softening
dc.subjectOrdinal optimization
dc.typeArticle
dc.contributor.departmentINDUSTRIAL & SYSTEMS ENGINEERING
dc.description.doi10.1023/A:1015317022797
dc.description.sourcetitleJournal of Optimization Theory and Applications
dc.description.volume113
dc.description.issue3
dc.description.page597-620
dc.identifier.isiut000175366000009
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.