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Description: Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects,Nonlinear Optimizationis the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates the theory and the methods of nonlinear optimization in a unified, clear, and mathematically rigorous fashion, with detailed and easy-to-follow proofs illustrated by numerous examples and figures. The book covers convex analysis, the theory of optimality conditions, duality theory, and numerical methods for solving unconstrained and constrained optimization problems. It addresses not only classical material but also modern topics such as optimality conditions and numerical methods for problems involving nondifferentiable functions, semidefinite programming, metric regularity and stability theory of set-constrained systems, and sensitivity analysis of optimization problems. Based on a decade's worth of notes the author compiled in successfully teaching the subject, this book will help readers to understand the mathematical foundations of the modern theory and methods of nonlinear optimization and to analyze new problems, develop optimality theory for them, and choose or construct numerical solution methods. It is a must for anyone seriously interested in optimization.
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
List price: $105.00
Copyright year: 2006
Publisher: Princeton University Press
Publication date: 1/22/2006
Size: 6.50" wide x 10.00" long x 1.50" tall
|Elements of Convex Analysis|
|Unconstrained Minima of Differentiable Functions|
|Unconstrained Minima of Convex Functions|
|Optimality Conditions for Smooth Problems|
|Optimality Conditions for Convex Problems|
|Optimality Conditions for Smooth-Convex Problems|
|Second Order Optimality Conditions|
|The Dual Problem|
|Convex Relaxation of Nonconvex Problems|
|The Optimal Value Function|
|The Augmented Lagrangian|
|Unconstrained Optimization of Differentiable Functions|
|Introduction to Iterative Algorithms|
|The Method of Steepest Descent|
|The Conjugate Gradient Method|
|Trust Region Methods|
|Constrained Optimization of Differentiable Functions|
|Feasible Point Methods|
|The Basic Dual Method|
|The Augmented Lagrangian Method|
|The Subgradient Method|
|The Cutting Plane Method|
|The Proximal Point Method|
|The Bundle Method|
|The Trust Region Method|
|Stability of Set-Constrained Systems|
|Set-Constrained Linear Systems|
|Set-Constrained Nonlinear Systems|