Nonlinear Optimization / / Andrzej Ruszczynski.

• Superior document: Title is part of eBook package: De Gruyter Princeton University Press eBook-Package Backlist 2000-2013
• VerfasserIn: Ruszczynski, Andrzej,
• Place / Publishing House: Princeton, NJ : : Princeton University Press, , [2011]; ©2006
• Year of Publication: 2011
• Language: English
• Physical Description: 1 online resource (464 p.) :; 35 line illus.
• Other title: Frontmatter --; Contents --; Preface --; Chapter One. Introduction --; PART 1. Theory --; Chapter Two. Elements of Convex Analysis --; Chapter Three. Optimality Conditions --; Chapter Four. Lagrangian Duality --; PART 2. Methods --; Chapter Five. Unconstrained Optimization of Differentiable Functions --; Chapter Six. Constrained Optimization of Differentiable Functions --; Chapter Seven. Nondifferentiable Optimization --; Appendix A. Stability of Set-Constrained Systems --; Further Reading --; Bibliography --; Index
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781400841059; 9783110442502
• DOI: 10.1515/9781400841059?locatt=mode:legacy
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: Andrzej Ruszczynski.

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 Optimization is 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.