Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/5953
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | - |
dc.creator | Chen, X | - |
dc.creator | Wets, RJ | - |
dc.creator | Zhang, Y | - |
dc.date.accessioned | 2014-12-11T08:24:25Z | - |
dc.date.available | 2014-12-11T08:24:25Z | - |
dc.identifier.issn | 1052-6234 | - |
dc.identifier.uri | http://hdl.handle.net/10397/5953 | - |
dc.language.iso | en | en_US |
dc.publisher | Society for Industrial and Applied Mathematics | en_US |
dc.rights | © 2012 Society for Industrial and Applied Mathematics | en_US |
dc.subject | Stochastic variational inequalities | en_US |
dc.subject | Epi-convergence | en_US |
dc.subject | Lower semicontinuous | en_US |
dc.subject | Upper semicontinuous | en_US |
dc.subject | Semismooth | en_US |
dc.subject | Smoothing sample average approximation | en_US |
dc.subject | Expected residual minimization | en_US |
dc.subject | Stationary point | en_US |
dc.title | Stochastic variational inequalities : residual minimization smoothing sample average approximations | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 649 | - |
dc.identifier.epage | 673 | - |
dc.identifier.volume | 22 | - |
dc.identifier.issue | 2 | - |
dc.identifier.doi | 10.1137/110825248 | - |
dcterms.abstract | The stochastic variational inequality (VI) has been used widely in engineering and economics as an effective mathematical model for a number of equilibrium problems involving uncertain data. This paper presents a new expected residual minimization (ERM) formulation for a class of stochastic VI. The objective of the ERM-formulation is Lipschitz continuous and semismooth which helps us guarantee the existence of a solution and convergence of approximation methods. We propose a globally convergent (a.s.) smoothing sample average approximation (SSAA) method to minimize the residual function; this minimization problem is convex for the linear stochastic VI if the expected matrix is positive semidefinite. We show that the ERM problem and its SSAA problems have minimizers in a compact set and any cluster point of minimizers and stationary points of the SSAA problems is a minimizer and a stationary point of the ERM problem (a.s.). Our examples come from applications involving traffic flow problems. We show that the conditions we impose are satisfied and that the solutions, efficiently generated by the SSAA procedure, have desirable properties. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | SIAM journal on optimization, 2012, v. 22, no. 2, p. 649–673 | - |
dcterms.isPartOf | SIAM Journal on optimization | - |
dcterms.issued | 2012 | - |
dc.identifier.isi | WOS:000306100300017 | - |
dc.identifier.scopus | 2-s2.0-84865693836 | - |
dc.identifier.eissn | 1095-7189 | - |
dc.identifier.rosgroupid | r58448 | - |
dc.description.ros | 2011-2012 > Academic research: refereed > Publication in refereed journal | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Chen_Stochastic_Variational_Inequalities.pdf | 421.96 kB | Adobe PDF | View/Open |
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