Engineering Optimization Theory and Practice 5th edition by Singiresu Rao- Ebook PDF Instant Download/Delivery. 9781119454793, 1119454794
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ISBN 10: 1119454794
ISBN 13: 9781119454793
Author: Singiresu S. Rao
The thoroughly revised and updated fifth edition of Engineering Optimization: Theory and Practice offers engineers a guide to the important optimization methods that are commonly used in a wide range of industries. The author–a noted expert on the topic–presents both the classical and most recent optimizations approaches. The book introduces the basic methods and includes information on more advanced principles and applications.
The fifth edition presents four new chapters: Solution of Optimization Problems Using MATLAB; Metaheuristic Optimization Methods; Multi-Objective Optimization Methods; and Practical Implementation of Optimization. All of the book’s topics are designed to be self-contained units with the concepts described in detail with derivations presented. The author puts the emphasis on computational aspects of optimization and includes design examples and problems representing different areas of engineering. Comprehensive in scope, the book contains solved examples, review questions and problems, and is accompanied by a website hosting a solutions manual. This important book:
- Offers an updated edition of the classic work on optimization
- Includes approaches that are appropriate for all branches of engineering
- Contains numerous practical design and engineering examples
- Offers more than 140 illustrative examples, 500 plus references in the literature of engineering optimization, and more than 500 review questions and answers
- Demonstrates the use of MATLAB for solving different types of optimization problems using different techniques
Written for students across all engineering disciplines, the revised edition of Engineering Optimization: Theory and Practice is the comprehensive book that covers the new and recent methods of optimization and reviews the principles and applications.
Engineering Optimization Theory and Practice 5th Table of contents:
1. Introduction to Optimization
- Introduction
- Historical Development
- Engineering Applications of Optimization
- Statement of An Optimization Problem
- Classification of Optimization Problems
- Optimization Techniques
- Engineering Optimization Literature
- Solutions Using MATLAB
2. Classical Optimization Techniques
- Introduction
- Single-Variable Optimization
- Multivariable Optimization with no Constraints
- Multivariable Optimization with Equality Constraints
- Multivariable Optimization with Inequality Constraints
- Convex Programming Problem
3. Linear Programming I: Simplex Method
- Introduction
- Applications of Linear Programming
- Standard form of a Linear Programming Problem
- Geometry of Linear Programming Problems
- Definitions and Theorems
- Solution of a System of Linear Simultaneous Equations
- Pivotal Reduction of a General System of Equations
- Motivation of the Simplex Method
- Simplex Algorithm
- Two Phases of the Simplex Method
- Solutions Using MATLAB
4. Linear Programming II: Additional Topics and Extensions
- Introduction
- Revised Simplex Method
- Duality in Linear Programming
- Decomposition Principle
- Sensitivity or Postoptimality Analysis
- Transportation Problem
- Karmarkar’s Interior Method
- Quadratic Programming
- Solutions Using MATLAB
5. Nonlinear Programming I: One-Dimensional Minimization Methods
- Introduction
- Unimodal Function
- Unrestricted Search
- Exhaustive Search
- Dichotomous Search
- Interval Halving Method
- Fibonacci Method
- Golden Section Method
- Comparison of Elimination Methods
- Quadratic Interpolation Method
- Cubic Interpolation Method
- Direct Root Methods
- Practical Considerations
- Solutions Using MATLAB
6. Nonlinear Programming II: Unconstrained Optimization Techniques
- Introduction
- Random Search Methods
- Grid Search Method
- Univariate Method
- Pattern Directions
- Powell’s Method
- Simplex Method
- Gradient of a Function
- Steepest Descent Method
- Conjugate Gradient Method
- Newton’s Method
- Marquardt Method
- Quasi-Newton Methods
7. Nonlinear Programming III: Constrained Optimization Techniques
- Introduction
- Characteristics of a Constrained Problem
- Random Search Methods
- Complex Method
- Sequential Linear Programming
- Basic Approach in the Methods of Feasible Directions
- Zoutendijk’s Method of Feasible Directions
- Rosen’s Gradient Projection Method
- Generalized Reduced Gradient Method
- Sequential Quadratic Programming
8. Geometric Programming
- Introduction
- Posynomial
- Unconstrained Minimization Problem
- Primal and Dual Programs in the Case of Less-than Inequalities
- Geometric Programming with Mixed Inequality Constraints
9. Dynamic Programming
- Introduction
- Multistage Decision Processes
- Concept of Suboptimization and Principle of Optimality
10. Integer Programming
- Introduction
- Graphical Representation
- Gomory’s Cutting Plane Method
- Branch-and-Bound Method
11. Stochastic Programming
- Introduction
- Basic Concepts of Probability Theory
- Stochastic Linear Programming
- Stochastic Nonlinear Programming
12. Optimal Control and Optimality Criteria Methods
- Introduction
- Calculus of Variations
- Optimal Control Theory
13. Modern Methods of Optimization
- Introduction
- Genetic Algorithms
- Simulated Annealing
- Particle Swarm Optimization
- Ant Colony Optimization
14. Metaheuristic Optimization Methods
- Definitions
- Metaphors Associated with Metaheuristic Optimization Methods
15. Practical Aspects of Optimization
- Introduction
- Reduction of Size of an Optimization Problem
- Fast Reanalysis Techniques
16. Multilevel and Multiobjective Optimization
- Introduction
- Multilevel Optimization
- Parallel Processing
- Multiobjective Optimization
17. Solution of Optimization Problems Using MATLAB
- Introduction
- Solution of General Nonlinear Programming Problems
- Solution of Linear Programming Problems
- Solution of Quadratic Programming Problems
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