Overview

Stochastic programming offers models and methods for decision problems where some of the data are uncertain. These models have features and structural properties which are preferably exploited by SP methods within the solution process. This work contributes to the methodology for two-stage models. In these models, the objective function is given as an integral, whose integrand depends on a random vector, on its probability measure and on a decision. The main results of this work are as follows: after investigating duality relations for convex optimization problems with supply/demand and prices treated as parameters, a stability criterion is stated and proves subdifferentiability of the value function. This criterion is employed for proving the existence of bilinear functions which minorize/majorize the integrand. Additionally, these minorants/majorants support the integrand on generalized barycentres of simplicial faces of specially shaped polytopes and amount to an approach called the barycentric approximation scheme.

Full Product Details

Author:   Karl Frauendorfer
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   Softcover reprint of the original 1st ed. 1992
Volume:   392
Dimensions:   Width: 17.00cm , Height: 1.30cm , Length: 24.20cm
Weight:   0.425kg
ISBN:  

9783540560975

ISBN 10:   3540560971
Pages:   228
Publication Date:   17 December 1992
Audience:   College/higher education ,  Professional and scholarly ,  Postgraduate, Research & Scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

0 Preliminaries.- I Stochastic Two-Stage Problems.- 1 Convex Case.- 2 Nonconvex Case.- 3 Stability.- 4 Epi-Convergence.- 5 Saddle Property.- 6 Stochastic Independence.- 7 Special Convex Cases.- II Duality and Stability in Convex Optimization (Extended Results for the Saddle Case).- 8 Characterization and Properties of Saddle Functions.- 9 Primal and Dual Collections of Programs.- 10 Normal and Stable Programs.- 11 Relation to McLinden's Results.- 12 Application to Convex Programming.- III Barycentric Approximation.- 13 Inequalities and Extremal Probability Measures - Convex Case.- 14 Inequalities and Extremal Probability Measures - Saddle Case.- 15 Examples and Geometric Interpretation.- 16 Iterated Approximation and x-Simplicial Refinement.- 17 Application to Stochastic Two-Stage Programs.- 18 Convergence of Approximations.- 19 Refinement Strategy.- 20 Iterative Completion.- IV An Illustrative Survey of Existing Approaches in Stochastic Two-Stage Programming.- 21 Error Bounds for Stochastic Programs with Recourse (due to Kali & Stoyan).- 22 Approximation Schemes discussed by Birge & Wets.- 23 Sublinear Bounding Technique (due to Birge & Wets).- 24 Stochastic Quasigradient Techniques (due to Ermoliev).- 25 Semi-Stochastic Approximation (due to Marti).- 26 Benders' Decomposition with Importance Sampling (due to Dantzig & Glynn).- 27 Stochastic Decomposition (due to Higle & Sen).- 28 Mathematical Programming Techniques.- 29 Scenarios and Policy Aggregation (due to Rockafellar & Wets).- V BRAIN - BaRycentric Approximation for Integrands (Implementation Issues).- 30 Storing Distributions given through a Finite Set of Parameters.- 31 Evaluation of Initial Extremal Marginal Distributions.- 32 Evaluation of Initial Outer and Inner Approximation.- 33 Data for x-Simplicial Partition.- 34 Evaluation of Extremal Distributions - Iteration!.- 35 Evaluation of Outer and Inner Approximation - Iteration J.- 36 x-Simplicial Refinement.- VI Solving Stochastic Linear Two-Stage Problems (Numerical Results and Computational Experiences).- 37 Testproblems from Literature.- 38 Randomly Generated Testproblems.

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