Asymptotic Theory for Solutions in Generalized M-Estimation and Stochastic Programming

King AJ & Rockafellar RT (1990). Asymptotic Theory for Solutions in Generalized M-Estimation and Stochastic Programming. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-90-076

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Abstract

New techniques of local sensitivity analysis in nonsmooth optimization are applied to the problem of studying the asymptotic behavior (generally non-normal) for solutions in stochastic optimization, and generalized M-estimation -- a reformulation of the traditional maximum-likelihood problem that allows the introduction of hard constraints.

Item Type: Monograph (IIASA Working Paper)
Research Programs: Adaption and Optimization (ADO)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:00
Last Modified: 13 Aug 2016 20:35
URI: http://pure.iiasa.ac.at/3379

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