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

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

[thumbnail of WP-90-076.pdf]
Preview
Text
WP-90-076.pdf

Download (483kB) | Preview

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: 27 Aug 2021 17:13
URI: https://pure.iiasa.ac.at/3379

Actions (login required)

View Item View Item