Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side. There is a selected solutions manual for instructors for the new edition. Table of Contents 1. Preface 2. Preface to the Second Edition 3. Introduction 4. Fundamentals of Unconstrained Optimization 5. Line Search Methods 6. rust-Region Methods 7. Conjugate Gradient Methods 8. Quasi-Newton Methods 9. Large-Scale Unconstrained Optimization 10. Calculating Derivatives 11. Derivative-Free Optimization 12. Least-Squares Problems 13. Nonlinear Equations 14. Theory of Constrained Optimization 15. Linear Programming: The Simplex Method 16. Linear Programming: Interior-Point Methods 17. Fundamentals of Algorithms for Nonlinear Constrained Optimization 18. Quadratic Programming 19. Penalty and Augmented Lagrangian Methods 20. Sequential Quadratic Programming 21. Interior-Point Methods for Nonlinear Programming 22. Background Material 23. Regularization Proc