This textbook presents a strong and clear relationship between theory and practice. It covers basic topics such as Dantzig's simplex algorithm, duality, sensitivity analysis, integer optimization models, and network models as well as more advanced topics including interior point algorithms, the branch-and-bound algorithm, cutting planes, and complexity. Along with case studies, it also discusses more advanced techniques such as column generation, multiobjective optimization, and game theory. It also includes computer code in the form of models in GMPL. The book contains appendices covering…mehr
This textbook presents a strong and clear relationship between theory and practice. It covers basic topics such as Dantzig's simplex algorithm, duality, sensitivity analysis, integer optimization models, and network models as well as more advanced topics including interior point algorithms, the branch-and-bound algorithm, cutting planes, and complexity. Along with case studies, it also discusses more advanced techniques such as column generation, multiobjective optimization, and game theory. It also includes computer code in the form of models in GMPL. The book contains appendices covering mathematical proofs, linear algebra, graph theory, convexity, and a background in nonlinear optimization. All chapters contain extensive examples and exercises. .
Gerard Sierksma, PhD, University of Groningen, The Netherlands Yori Zwols, PhD, Google UK, London
Inhaltsangabe
Basic Concepts of Linear Optimization. LINEAR OPTIMIZATION THEORY: BASIC TECHNIQUES. Geometry and Algebra of Feasible Regions. Dantzig's Simplex Algorithm. Duality, Feasibility, and Optimality. Sensitivity Analysis. Large-Scale Linear Optimization. Integer Linear Optimization. Linear Network Models. Computational Complexity. LINEAR OPTIMIZATION PRACTICE: ADVANCED TECHNIQUES. Designing a Reservoir for Irrigation. Classifying Documents by Language. Production Planning; A Single Product Case. Production of Coffee Machines. Conflicting Objectives: Producing Versus Importing. Coalition Formation and Profit Distribution. Minimizing Trimloss When Cutting Cardboard. Off-Shore Helicopter Routing. The Catering Service Problem. Appendix A Mathematical Proofs. Appendix B Linear Algebra. Appendix C Graph Theory. Appendix D Convexity. Appendix E Nonlinear Optimization. Appendix F Writing LO-Models in GNU MathProg (GMPL). List of Symbols. Bibliography.
Basic Concepts of Linear Optimization. LINEAR OPTIMIZATION THEORY: BASIC TECHNIQUES. Geometry and Algebra of Feasible Regions. Dantzig's Simplex Algorithm. Duality, Feasibility, and Optimality. Sensitivity Analysis. Large-Scale Linear Optimization. Integer Linear Optimization. Linear Network Models. Computational Complexity. LINEAR OPTIMIZATION PRACTICE: ADVANCED TECHNIQUES. Designing a Reservoir for Irrigation. Classifying Documents by Language. Production Planning; A Single Product Case. Production of Coffee Machines. Conflicting Objectives: Producing Versus Importing. Coalition Formation and Profit Distribution. Minimizing Trimloss When Cutting Cardboard. Off-Shore Helicopter Routing. The Catering Service Problem. Appendix A Mathematical Proofs. Appendix B Linear Algebra. Appendix C Graph Theory. Appendix D Convexity. Appendix E Nonlinear Optimization. Appendix F Writing LO-Models in GNU MathProg (GMPL). List of Symbols. Bibliography.
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