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It is vitally important to meet or exceed previous quality and reliability standards while at the same time reducing resource consumption. This textbook addresses this critical imperative integrating theory, modeling, the development of numerical methods, and problem solving, thus preparing the student to apply optimization to real-world problems. This text covers a broad variety of optimization problems using: unconstrained, constrained, gradient, and non-gradient techniques; duality concepts; multiobjective optimization; linear, integer, geometric, and dynamic programming with applications; and finite element–based optimization. In this revised and enhanced second edition of Optimization Concepts and Applications in Engineering, the already robust pedagogy has been enhanced with more detailed explanations, an increased number of solved examples and end-of-chapter problems. The source codes are now available free on multiple platforms. It is ideal for advanced undergraduate or graduate courses and for practicing engineers in all engineering disciplines, as well as in applied mathematics.Read more
- Computer graphics and code implementation are illustrated in detail. While Matlab and Excel are used in the text examples, the understanding carries over to use of programs in the CD-ROM or commercial software readily
- Connects theory to practice by having theory implemented in accompanying source code computer programs, inclusion of a number of solved examples in each chapter and stressing steps in modelling
- Teaches students to understand how to define an objective function, especially in the presence of conflicting objectives
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- Edition: 2nd Edition
- Date Published: March 2011
- format: Hardback
- isbn: 9780521878463
- length: 480 pages
- dimensions: 254 x 178 x 27 mm
- weight: 0.92kg
- contains: 203 b/w illus. 17 tables 256 exercises
- availability: Available
Table of Contents
1. Preliminary concepts
2. One dimensional unconstrained minimization
3. Unconstrained optimization
4. Linear programming
5. Constrained minimization
6. Penalty functions, duality, and geometric programming
7. Direct search methods for nonlinear optimization
8. Multiobjective optimization
9. Integer and discrete programming
10. Dynamic programming
11. Optimization applications for transportation, assignment, and network problems
12. Finite element based optimization.
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