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| Preface | ||
| O | Preliminaries | 1 |
| Pt. I | Stochastic Two-Stage Problems | 11 |
| 1 | Convex Case | 14 |
| 2 | Nonconvex Case | 17 |
| 3 | Stability | 20 |
| 4 | Epi-Convergence | 22 |
| 5 | Saddle Property | 25 |
| 6 | Stochastic Independence | 30 |
| 7 | Special Convex Cases | 32 |
| Pt. II | Duality and Stability in Convex Optimization (Extended Results for the Saddle Case) | 37 |
| 8 | Characterization and Properties of Saddle Functions | 41 |
| 9 | Primal and Dual Collections of Programs | 46 |
| 10 | Normal and Stable Programs | 54 |
| 11 | Relation to McLinden's Results | 58 |
| 12 | Application to Convex Programming | 62 |
| Pt. III | Barycentric Approximation | 67 |
| 13 | Inequalities and Extremal Probability Measures - Convex Case | 71 |
| 14 | Inequalities and Extremal Probability Measures - Saddle Case | 80 |
| 15 | Examples and Geometric Interpretation | 99 |
| 16 | Iterated Approximation and x-Simplicial Refinement | 106 |
| 17 | Application to Stochastic Two-Stage Programs | 113 |
| 18 | Convergence of Approximations | 116 |
| 19 | Refinement Strategy | 118 |
| 20 | Iterative Completion | 122 |
| Pt. IV | An Illustrative Survey of Existing Approaches in Stochastic Two-Stage Programming | 125 |
| 21 | Error Bounds for Stochastic Programs with Recourse (due to Kall & Stoyan) | 127 |
| 22 | Approximation Schemes discussed by Birge & Wets | 131 |
| 23 | Sublinear Bounding Technique (due to Birge & Wets) | 134 |
| 24 | Stochastic Quasigradient Techniques (due to Ermoliev) | 137 |
| 25 | Semi-Stochastic Approximation (due to Marti) | 139 |
| 26 | Benders' Decomposition with Importance Sampling (due to Dantzig & Glynn) | 141 |
| 27 | Stochastic Decomposition (due to Higle & Sen) | 146 |
| 28 | Mathematical Programming Techniques | 149 |
| 29 | Scenarios and Policy Aggregation (due to Rockafellar & Wets) | 151 |
| Pt. V | BRAIN - BaRycentric Approximation for INtegrands (Implementation Issues) | 159 |
| 30 | Storing Distributions given through a Finite Set of Parameters | 161 |
| 31 | Evaluation of Initial Extremal Marginal Distributions | 163 |
| 32 | Evaluation of Initial Outer and Inner Approximation | 167 |
| 33 | Data for x-Simplicial Partition | 169 |
| 34 | Evaluation of Extremal Distributions - Iteration J | 174 |
| 35 | Evaluation of Outer and inner Approximation - Iteration J | 178 |
| 36 | x-Simplicial Refinement | 179 |
| Pt. VI | Solving Stochastic Linear Two-Stage Problems (Numerical Results and Computational Experiences) | 185 |
| 37 | Testproblems from Literature | 188 |
| 38 | Randomly Generated Testproblems | 199 |
| Bibliography | 219 |
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Add Stochastic Two-Stage Programming, 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 contri, Stochastic Two-Stage Programming to the inventory that you are selling on WonderClubX
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Add Stochastic Two-Stage Programming, 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 contri, Stochastic Two-Stage Programming to your collection on WonderClub |