Andere Kunden interessierten sich auch für
![Linear Optimization for Business]( "Linear Optimization for Business")
Linear Optimization for Business91,99 €![Optimization of Supply Chain Management in Contemporary Organizations]( "Optimization of Supply Chain Management in Contemporary Organizations")
Optimization of Supply Chain Management in Contemporary Organizations220,99 €![Operations Strategy]( "Operations Strategy")
Operations Strategy85,99 €![Introduction to Nature-Inspired Optimization]( "Introduction to Nature-Inspired Optimization")
Introduction to Nature-Inspired Optimization129,99 €![Quantitative Models for Supply Chain Management]( "Quantitative Models for Supply Chain Management")
Quantitative Models for Supply Chain Management367,99 €![Optimality Conditions in Convex Optimization]( "Optimality Conditions in Convex Optimization")
Optimality Conditions in Convex Optimization70,99 €![Fractional Difference, Differential Equations, and Inclusions]( "Fractional Difference, Differential Equations, and Inclusions")
Fractional Difference, Differential Equations, and Inclusions191,99 €
For first courses in operations research, operations management.
Covers a broad range of optimization techniques, including linear programming, network flows, integer/combinational optimization, and nonlinear programming. Emphasizes the importance of modeling and problem formulation, this text teaches students how to apply algorithms to real-world problems to arrive at optimal solutions.
Visit the author-maintained web site athttp://comp.uark.edu/~rrardin/oorbook
Features + Benefits
Integrates treatment of network flows, integer and combinatorial optimization, and nonlinear programming with coverage of standard linear programming topics:
Prepares students for the variety of model forms needed in practice and provides instructors with flexibility in depth of coverage (Chapter 2).
Develops algorithms around a unified improving search theme, providing students with a paradigm that will extend into more advanced cases:
Allows instructors to efficiently treat both simplex and interior-point methods for linear programming (Chapter 3).
Integrates modeling and formulation with coverage of optimization algorithms to develop an understanding of formulation skills, algorithms and principles simultaneously. Pg.___
Highlights all main definitions, principles and algorithms in special-interest boxes. Pg.___
Features one- to two-page Primers reviewing prerequisite material where necessary. Pg.___
Sample exercises recap constructions and computations of longer developments. Pg.___
Includes a wealth of charts, tables and figures. Pg.___
Exercises at the ends of chapters provide practice with calculations, concepts, and formulation of small problems, in addition to developing modeling skills with more challenging problems drawn from O. R. practice. Pg.___
Selected answers are provided at the back of the book and an Instructor's Manual is available with full solutions.
1. Problem Solving with Mathematical Models.
2. Deterministic Optimization Models in Operations Research.
3. Improving Search.
4. Linear Programming Models.
5. Simplex Search for Linear Programming.
6. Interior Point Methods for Linear Programming.
7. Duality and Sensitivity in Linear Programming.
8. Multiobjective Optimization and Goal Programming.
9. Shortest Path and Discrete Dynamic Programming.
10. Network Flows.
11. Discrete Optimization Models.
12. Discrete Optimization Methods.
13. Unconstrained Nonlinear Programming.
14. Constrained Nonlinear Programming.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Covers a broad range of optimization techniques, including linear programming, network flows, integer/combinational optimization, and nonlinear programming. Emphasizes the importance of modeling and problem formulation, this text teaches students how to apply algorithms to real-world problems to arrive at optimal solutions.
Visit the author-maintained web site athttp://comp.uark.edu/~rrardin/oorbook
Features + Benefits
Integrates treatment of network flows, integer and combinatorial optimization, and nonlinear programming with coverage of standard linear programming topics:
Prepares students for the variety of model forms needed in practice and provides instructors with flexibility in depth of coverage (Chapter 2).
Develops algorithms around a unified improving search theme, providing students with a paradigm that will extend into more advanced cases:
Allows instructors to efficiently treat both simplex and interior-point methods for linear programming (Chapter 3).
Integrates modeling and formulation with coverage of optimization algorithms to develop an understanding of formulation skills, algorithms and principles simultaneously. Pg.___
Highlights all main definitions, principles and algorithms in special-interest boxes. Pg.___
Features one- to two-page Primers reviewing prerequisite material where necessary. Pg.___
Sample exercises recap constructions and computations of longer developments. Pg.___
Includes a wealth of charts, tables and figures. Pg.___
Exercises at the ends of chapters provide practice with calculations, concepts, and formulation of small problems, in addition to developing modeling skills with more challenging problems drawn from O. R. practice. Pg.___
Selected answers are provided at the back of the book and an Instructor's Manual is available with full solutions.
1. Problem Solving with Mathematical Models.
2. Deterministic Optimization Models in Operations Research.
3. Improving Search.
4. Linear Programming Models.
5. Simplex Search for Linear Programming.
6. Interior Point Methods for Linear Programming.
7. Duality and Sensitivity in Linear Programming.
8. Multiobjective Optimization and Goal Programming.
9. Shortest Path and Discrete Dynamic Programming.
10. Network Flows.
11. Discrete Optimization Models.
12. Discrete Optimization Methods.
13. Unconstrained Nonlinear Programming.
14. Constrained Nonlinear Programming.
Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.