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 1โ31):
Chapter 2 Functions of a Single Variable (pages 32โ77):
Chapter 3 Functions of Several Variables (pages 78โ148):
Chapter 4 Linear Programming (pages 149โ217):
Chapter 5 Constrained Optimality Criteria (pages 218โ259):
Chapter 6 Transformation Methods (pages 260โ304):
Chapter 7 Constrained Direct Search (pages 305โ335):
Chapter 8 Linearization Methods for Constrained Problems (pages 336โ377):
Chapter 9 Direction Generation Methods Based on Linearization (pages 378โ449):
Chapter 10 Quadratic Approximation Methods for Constrained Problems (pages 450โ480):
Chapter 11 Structured Problems and Algorithms (pages 481โ529):
Chapter 12 Comparison of Constrained Optimization Methods (pages 530โ541):
Chapter 13 Strategies for Optimization Studies (pages 542โ602):
Chapter 14 Engineering Case Studies (pages 603โ632):