Engineering Optimization: Methods and Applications, Second Edition

Description

The classic introduction to engineering optimization theory and practice--now expanded and updated Engineering optimization helps engineers zero in on the most effective, efficient solutions to problems. This text provides a practical, real-world understanding of engineering optimization. Rather than belaboring underlying proofs and mathematical derivations, it emphasizes optimization methodology, focusing on techniques and stratagems relevant to engineering applications in design, operations, and analysis. It surveys diverse optimization methods, ranging from those applicable to the minimization of a single-variable function to those most suitable for large-scale, nonlinear constrained problems. New material covered includes the duality theory, interior point methods for solving LP problems, the generalized Lagrange multiplier method and generalization of convex functions, and goal programming for solving multi-objective optimization problems. A practical, hands-on reference and text, Engineering Optimization, Second Edition covers: * Practical issues, such as model formulation, implementation, starting point generation, and more * Current, state-of-the-art optimization software * Three engineering case studies plus numerous examples from chemical, industrial, and mechanical engineering * Both classical methods and new techniques, such as successive quadratic programming, interior point methods, and goal programming Excellent for self-study and as a reference for engineering professionals, this Second Edition is also ideal for senior and graduate courses on engineering optimization, including television and online instruction, as well as for in-plant training.Content: Chapter 1 Introduction to Optimization (pages 1–31): Chapter 2 Functions of a Single Variable (pages 32–77): Chapter 3 Functions of Several Variables (pages 78–148): Chapter 4 Linear Programming (pages 149–217): Chapter 5 Constrained Optimality Criteria (pages 218–259): Chapter 6 Transformation Methods (pages 260–304): Chapter 7 Constrained Direct Search (pages 305–335): Chapter 8 Linearization Methods for Constrained Problems (pages 336–377): Chapter 9 Direction Generation Methods Based on Linearization (pages 378–449): Chapter 10 Quadratic Approximation Methods for Constrained Problems (pages 450–480): Chapter 11 Structured Problems and Algorithms (pages 481–529): Chapter 12 Comparison of Constrained Optimization Methods (pages 530–541): Chapter 13 Strategies for Optimization Studies (pages 542–602): Chapter 14 Engineering Case Studies (pages 603–632):