Description

Book Synopsis

​This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields.



Trade Review
“The historical notes in the book are interesting and well placed. … The book’s list of important references is quite complete. … this book is destined to become a classic in the field for beginning graduate students in optimization.” (S. Zlobec, Mathematical Reviews, January, 2018)



Table of Contents
Chapter 1. LP Models and Applications.- Chapter 2. Linear Equations and Inequalities.- Chapter 3. The Simplex Algorithm.- Chapter 4. The Simplex Algorithm Continued.- Chapter 5. Duality and the Dual Simplex Algorithm.- Chapter 6. Postoptimality Analysis.- Chapter 7. Some Computational Considerations.- Chapter 8. NLP Models and Applications.- Chapter 9. Unconstrained Optimization.- Chapter 10. Descent Methods.- Chapter 11. Optimality Conditions.- Chapter 12. Problems with Linear Constraints.- Chapter 13. Problems with Nonlinear Constraints.- Chapter 14. Interior-Point Methods.

Linear and Nonlinear Optimization 253 International Series in Operations Research Management Science

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    A Paperback by Richard W. Cottle, Mukund N. Thapa

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      View other formats and editions of Linear and Nonlinear Optimization 253 International Series in Operations Research Management Science by Richard W. Cottle

      Publisher: Springer New York
      Publication Date: 5/12/2018 12:00:00 AM
      ISBN13: 9781493983797, 978-1493983797
      ISBN10: 1493983792

      Description

      Book Synopsis

      ​This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields.



      Trade Review
      “The historical notes in the book are interesting and well placed. … The book’s list of important references is quite complete. … this book is destined to become a classic in the field for beginning graduate students in optimization.” (S. Zlobec, Mathematical Reviews, January, 2018)



      Table of Contents
      Chapter 1. LP Models and Applications.- Chapter 2. Linear Equations and Inequalities.- Chapter 3. The Simplex Algorithm.- Chapter 4. The Simplex Algorithm Continued.- Chapter 5. Duality and the Dual Simplex Algorithm.- Chapter 6. Postoptimality Analysis.- Chapter 7. Some Computational Considerations.- Chapter 8. NLP Models and Applications.- Chapter 9. Unconstrained Optimization.- Chapter 10. Descent Methods.- Chapter 11. Optimality Conditions.- Chapter 12. Problems with Linear Constraints.- Chapter 13. Problems with Nonlinear Constraints.- Chapter 14. Interior-Point Methods.

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