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Antoniou A., Lu W.-S. Practical Optimization. Algorithms and Engineering Applications

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Antoniou A., Lu W.-S. Practical Optimization. Algorithms and Engineering Applications
Издательство Springer, 2007, -675 pp.
The rapid advancements in the efficiency of digital computers and the evolution of reliable software for numerical computation during the past three decades have led to an astonishing growth in the theory, methods, and algorithms of numerical optimization. This body of knowledge has, in turn, motivated widespread applications of optimization methods in many disciplines, e.g., engineering, business, and science, and led to problem solutions that were considered intractable not too long ago.
Although excellent books are available that treat the subject of optimization with great mathematical rigor and precision, there appears to be a need for a book that provides a practical treatment of the subject aimed at a broader audience ranging from college students to scientists and industry professionals. This book has been written to address this need. It treats unconstrained and constrained optimization in a unified manner and places special attention on the algorithmic aspects of optimization to enable readers to apply the various algorithms and methods to specific problems of interest. To facilitate this process, the book provides many solved examples that illustrate the principles involved, and includes, in addition, two chapters that deal exclusively with applications of unconstrained and constrained optimization methods to problems in the areas of pattern recognition, control systems, robotics, communication systems, and the design of digital filters. For each application, enough background information is provided to promote the understanding of the optimization algorithms used to obtain the desired solutions.
The Optimization Problem
Basic Principles
General Properties of Algorithms
One-Dimensional Optimization
Basic Multidimensional Gradient Methods
Conjugate-Direction Methods
Quasi-Newton Methods
Minimax Methods
Applications of Unconstrained Optimization
Fundamentals of Constrained Optimization
Linear Programming Part I: The Simplex Method
Linear Programming Part II: Interior-Point Methods
Quadratic and Convex Programming
Semidefinite and Second-order Cone Programming
General Nonlinear Optimization Problems
Applications of Constrained Optimization
A: Basics of Linear Algebra
B: Basics of Digital Filters
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