Stochastic two-stage programming
著者
書誌事項
Stochastic two-stage programming
（Lecture notes in economics and mathematical systems, 392）
Springer-Verlag, 1992
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- : us
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注記
Includes bibliographical references (p. 219-228)
内容説明・目次
内容説明
Stochastic Programming offers models and methods for
decision problems wheresome 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-stagemodels. 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 have been derived with the
intention to ease these difficulties: After investigating
duality relations for convex optimization problems with
supply/demand and prices being 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
barycenters of simplicial faces of specially shaped
polytopes and amount to an approach which is denoted
barycentric approximation scheme.
目次
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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