Linear And Nonlinear Programming 3rd Edition By David G Luenberger

Linear and Nonlinear Programming (3rd Edition)

Author: David G. Luenberger
Binding: Paperback
Paper Quality: White Paper
Category: Optimization, Applied Mathematics, Engineering
Recommended For:
BS/MS students in Mathematics, Computer Science, Engineering, Operations Research, and Economics; essential for researchers and professionals working in optimization and decision science.

Key Points:

1. Optimization Foundations The book starts with the basics of optimization theory, laying out the core principles and mathematical foundations necessary for understanding both linear and nonlinear programming problems.

2. Linear Programming Detailed coverage of linear programming includes simplex methods, duality theory, and sensitivity analysis, which are essential for solving optimization problems with linear constraints and objective functions.

3. Convex Optimization The text introduces convex optimization theory, emphasizing the significance of convexity in simplifying problem-solving and ensuring global optima in nonlinear programming.

4. Duality Theory Extensive discussion on duality theory explores the relationships between primal and dual problems, providing insights into problem complexity and solution methods.

5. Nonlinear Programming Nonlinear programming techniques are covered, including methods for solving problems where the objective function or constraints are nonlinear, with a focus on algorithms and practical implementation.

6. Algorithms and Methods The book presents a variety of algorithms for both linear and nonlinear programming, including interior-point methods and gradient-based techniques, with detailed explanations and examples.

7. Computational Tools Practical aspects of implementing algorithms using computational tools and software are discussed, bridging the gap between theoretical concepts and real-world applications.

8. Applications and Case Studies Real-world applications and case studies demonstrate how the optimization techniques discussed are applied in various fields, providing context and practical relevance to the theory.

9. Advanced Topics Advanced topics, such as stochastic programming and global optimization, are included, offering a broader perspective on the challenges and solutions in complex optimization problems.

10. Mathematical Programming Models The book covers various mathematical programming models, including integer programming and dynamic programming, highlighting their relevance and application in different scenarios.

Conclusion: "Linear and Nonlinear Programming" serves as a comprehensive resource for students and professionals seeking to understand and apply optimization techniques. With its thorough treatment of both linear and nonlinear problems, algorithms, and practical applications, the book provides valuable insights and tools for solving complex optimization challenges in various disciplines.

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Writer                 ✤             David G Luenberger & Yinyu Ye