Ronald L. Rardin

Indeholder "Preface", "About the Author", "Chapter 1. Problem Solving With Mathematical Models", " 1.1 OR Application Stories", " 1.2 Optimization and the Operations Research Process", " 1.3 System Boundaries, Sensitivity Analysis, Tractability and Validity", " 1.4 Descriptive Models and Simulation", " 1.5 Numerical Search and Exact versus Heuristic Solutions", " 1.6 Deterministic versus Stochastic Models", " 1.7 Perspectives", " Exercises", "Chapter 2. Deterministic Optimization Models in show more Operations Research", " 2.1 Decision Variables, Constraints, and Objective Functions", " 2.2 Graphic Solution and Optimization Outcomes", " 2.3 Large-Scale Optimization Models and Indexing", " 2.4 Linear and Nonlinear Programs", " 2.5 Discrete or Integer Programs", " 2.6 Multiobjective Optimization Models", " 2.7 Classification Summary", " Exercises", "Chapter 3. Improving Search", " 3.1 Improving Search, Local and Global Optima", " 3.2 Search with Improving and Feasible Directions", " 3.3 Algebraic Conditions for Improving and Feasible Directions", " 3.4 Unimodel and Convex Model Forms Tractable for Improving Search", " 3.5 Searching and Starting Feasible Solutions", " Exercises", "Chapter 4. Linear Programming Models", " 4.1 Allocation Models", " 4.2 Blending Models", " 4.3 Operations Planning Models", " 4.4 Shift Scheduling and Staff Planning Models", " 4.5 Time-Phased Models", " 4.6 Models with Linearizable Nonlinear Objectives", "Exercises", "Chapter 5. Simplex Search for Linear Programming", " 5.1 LP Optimal Solutions and Standard Form", " 5.2 Extreme-Point Search and Basic Solutions", " 5.3 The Simplex Algorithm", " 5.4 Dictionary and Tableau Representations of Simplex", " 5.5 Two Phase Simplex", " 5.6 Degeneracy and Zero-Length Simplex Steps", " 5.7 Convergence and Cycling with Simplex", " 5.8 Doing It Efficiently: Revised Simplex", " Exercises", "Chapter 6. Interior Point Methods for Linear Programming", " 6.1 Searching through the Interior", " 6.2 Scaling with the Current Solution", " 6.3 Affine Scaling Search", " 6.4 Log Barrier Methods for Interior Point Search", " 6.5 Dual and Primal-Dual Extensions", " Exercises", "Chapter 7. Duality and Sensitivity in Linear Programming", " 7.1 Generic Activities versus Resources Perspective", " 7.2 Qualitative Sensitivity to Changes in Model Coefficients", " 7.3 Quantifying Sensitivity to Changes in LP Model Coefficients: A Dual Model", " 7.4 Formulating Linear Programming Duals", " 7.5 Primal-to-Dual Relationships", " 7.6 Computer Outputs and What If Changes of Single Parameters", " 7.7 Bigger Model Changes, Reoptimization, and Parametric Programming", " Exercises", "Chapter 8. Multiobjective Optimization and Goal Programming", " 8.1 Multiobjective Optimization Models", " 8.2 Efficient Points and the Efficient Frontier", " 8.3 Preemptive Optimization and Weighted Sums of Objectives", " 8.4 Goal Programming", " Exercises", "Chapter 9. Shortest Paths and Discrete Dynamic Programming", " 9.1 Shortest Path Models", " 9.2 Dynamic Programming Approach to Shortest Paths", " 9.3 Shortest Paths From One Node to All Others: Bellman-Ford", " 9.4 Shortest Paths From All Nodes to All Others: Floyd-Warshall", " 9.5 Shortest Path From One Node to All Others With Costs Nonnegative: Dijkstra", " 9.6 Shortest Paths From One Node to All Others in Acyclic Digraphs", " 9.7 CPM Project Scheduling and Longest Paths", " 9.8 Discrete Dynamic Programming Models", " Exercises", "Chapter 10. Network Flows", " 10.1 Graphs, Networks, and Flows", " 10.2 Cycle Directions for Network Flow Search", " 10.3 Rudimentary Cycle Direction Search Algorithms for Network Flows", " 10.4 Integrality of Optimal Network Flows", " 10.5 Transportation and Assignment Models", " 10.6 Other Single-Commodity Network Flow Models", " 10.7 Network Simplex Algorithm for Optimal Flows", " 10.8 Cycle Canceling Algorithms for Optimal Flows", " 10.9 Multicommodity and Gain/Loss Flows", " Exercises", "Chapter 11. Discrete Optimization Models", " 11.1 Lumpy Linear Programs and Fixed Charges", " 11.2 Knapsack and Capital Budgeting Models", " 11.3 Set Packing, Covering, and Partitioning Models", " 11.4 Assignment and Matching Models", " 11.5 Traveling Salesman and Routing Models", " Exercises", "Chapter 12. Discrete Optimization Methods", " 12.1 Solving by Total Enumeration", " 12.2 Relaxations of Discrete Optimization Models and Their Uses", " 12.3 Stronger LP relaxations, Valid Inequalities, and Lagrangian Relaxations", " 12.4 Branch and Bound Search", " 12.5 Rounding, Parent Bounds, Enumerations Sequences, and Stopping Early in Branch and Bound", " 12.6 Improving Search Heuristics for Discrete Optimization INLPs", " 12.7 Tabu, Simulated Annealing, and Genetic Algorithm Extensions of Improving Search", " 12.8 Constructive Heuristics", " Exercises", "Chapter 13. Unconstrained Nonlinear Programming", " 13.1 Unconstrained Nonlinear Programming Models", " 13.2 One-Dimensional Search", " 13.3 Derivatives, Taylor Series, and Conditions for Local Optima", " 13.4 Convex Concave Functions and Global Optimality", " 13.5 Gradient Search", " 13.6 Newton's Method", " 13.7 Quasi-Newton Methods and BFGS Search", " 13.8 Optimization without Derivatives and Nelder-Mead", " Exercises", "Chapter 14. Constrained Nonlinear Programming", " 14.1 Constrained Nonlinear Programming Models", " 14.2 Convex, Separable, Quadratic and Posynomial Geometric Programming Special NLP Forms", " 14.3 Lagrange Multiplier Methods", " 14.4 Karush-Kuhn-Tucker Optimality Conditions", " 14.5 Penalty and Barrier Methods", " 14.6 Reduced Gradient Algorithms", " 14.7 Quadratic Programming Methods", " 14.8 Separable Programming Methods", " 14.9 Posynomial Programming Methods", " Exercises", "Selected Answers", "Index".

Ganske spændende indblik i optimeringsproblemer i størrelse stor. Og ja, posynomial er ikke en stavefejl. show less