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Soft cover. Zustand: New. Contents: Preface. 1 Mathematical Preliminaries. 2 Optimality Conditions for Unconstrained Optimization. 3 Least Squares. 4 The Gradient Method. 5 Newton s Method. 6 Convex Sets. 7 Convex Functions. 8 Convex Optimization. 10 Optimality Conditions for Linearly Constrained Problems. 11 The KKT Conditions. 12 Duality. Bibliographic Notes. Bibliography. Index. This book provides the foundations of the theory of nonlinear optimization as well as some related algorithms and presents a variety of applications from diverse areas of applied sciences. The author combines three pillars of optimization theoretical and algorithmic foundation, familiarity with various applications, and the ability to apply the theory and algorithms on actual problems and rigorously and gradually builds the connection between theory, algorithms, applications, and implementation. Readers will find: More than 170 theoretical, algorithmic, and numerical exercises that deepen and enhance the reader's understanding of the topics; Several subjects not typically found in optimization books for example, optimality conditions in sparsity-constrained optimization, hidden convexity, and total least squares; A large number of applications discussed theoretically and algorithmically, such as circle fitting, Chebyshev center, the Fermat Weber problem, denoising, clustering, total least squares, and orthogonal regression; and Theoretical and algorithmic topics demonstrated by the MATLAB® toolbox CVX and a package of m-files that is posted on the book s web site. This book is intended for graduate or advanced undergraduate students of mathematics, computer science, and electrical engineering as well as other engineering disciplines. The book will also be of interest to researchers. (jacket).…

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Paperback. Zustand: New. This self-contained textbook provides the foundations of linear optimization, covering topics in both continuous and discrete linear optimization. It gradually builds the connection between theory, algorithms, and applications so that readers gain a theoretical and algorithmic foundation, familiarity with a variety of applications, and the ability to apply the theory and algorithms to actual problems.To deepen the reader's understanding, the authors provide many applications from diverse areas of applied sciences, such as resource allocation, line fitting, graph coloring, the traveling salesman problem, game theory, and network flows. The book also includes more than 180 exercises, most of them with partial answers and about 70 with complete solutions, as well as a continuous illustration of the theory through examples and exercises.A First Course in Linear Optimization is intended to be read cover to cover and requires only a first course in linear algebra as a prerequisite. Its 13 chapters can be used as lecture notes for a first course in linear optimization.AudienceThis book is for a first undergraduate course in linear optimization, such as linear programming, linear optimization, and operations research. It is appropriate for students in operations research, mathematics, economics, and industrial engineering, as well as those studying computer science and engineering disciplines.…

Paperback. Zustand: Brand New. 370 pages. 10.00x7.00x1.50 inches. In Stock.

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Paperback. Zustand: Brand New. 2nd edition. 354 pages. 10.04x7.05x0.87 inches. In Stock.

Paperback. Zustand: New. Second Edition. Built on the framework of the successful first edition, this book serves as a modern introduction to the field of optimization. The author's objective is to provide the foundations of theory and algorithms of nonlinear optimization as well as to present a variety of applications from diverse areas of applied sciences. Introduction to Nonlinear Optimization gradually yet rigorously builds connections between theory, algorithms, applications, and actual implementation. The book contains several topics not typically included in optimization books, such as optimality conditions in sparsity constrained optimization, hidden convexity, and total least squares. Readers will discover a wide array of applications such as circle fitting, Chebyshev center, the Fermat-Weber problem, denoising, clustering, total least squares, and orthogonal regression. These applications are studied both theoretically and algorithmically, illustrating concepts such as duality. Python and MATLAB programs are used to show how the theory can be implemented. The extremely popular CVX toolbox (MATLAB) and CVXPY module (Python) are described and used.More than 250 theoretical, algorithmic, and numerical exercises enhance the reader's understanding of the topics. (More than 70 of the exercises provide detailed solutions, and many others are provided with final answers.) The theoretical and algorithmic topics are illustrated by Python and MATLAB examples.…

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Zustand: New. 2017. paperback. . . . . .

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Paperback. Zustand: New. The primary goal of this book is to provide a self-contained, comprehensive study of the main ?rst-order methods that are frequently used in solving large-scale problems. First-order methods exploit information on values and gradients/subgradients (but not Hessians) of the functions composing the model under consideration. With the increase in the number of applications that can be modeled as large or even huge-scale optimization problems, there has been a revived interest in using simple methods that require low iteration cost as well as low memory storage.The author has gathered, reorganized, and synthesized (in a unified manner) many results that are currently scattered throughout the literature, many of which cannot be typically found in optimization books.First-Order Methods in Optimization offers comprehensive study of first-order methods with the theoretical foundations; provides plentiful examples and illustrations; emphasizes rates of convergence and complexity analysis of the main first-order methods used to solve large-scale problems; and covers both variables and functional decomposition methods.…

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Zustand: New. pp. 484.