1# Library to Find Best eBook for Read
Author : S. V. Gopala Krishna
Release : 1971
Genre :
Kind : eBook
Book Rating : /5 ( reviews)
Book Synopsis Nonlinear Optimization by the Sequential Unconstrained Minimization Technique Using Conjugate Gradient Methods by : S. V. Gopala Krishna
Download or read book Nonlinear Optimization by the Sequential Unconstrained Minimization Technique Using Conjugate Gradient Methods written by S. V. Gopala Krishna. This book was released on 1971. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Neculai Andrei
Release : 2020-06-29
Genre : Mathematics
Kind : eBook
Book Rating : 492/5 ( reviews)
Book Synopsis Nonlinear Conjugate Gradient Methods for Unconstrained Optimization by : Neculai Andrei
Download or read book Nonlinear Conjugate Gradient Methods for Unconstrained Optimization written by Neculai Andrei. This book was released on 2020-06-29. Available in PDF, EPUB and Kindle. Book excerpt: Two approaches are known for solving large-scale unconstrained optimization problems—the limited-memory quasi-Newton method (truncated Newton method) and the conjugate gradient method. This is the first book to detail conjugate gradient methods, showing their properties and convergence characteristics as well as their performance in solving large-scale unconstrained optimization problems and applications. Comparisons to the limited-memory and truncated Newton methods are also discussed. Topics studied in detail include: linear conjugate gradient methods, standard conjugate gradient methods, acceleration of conjugate gradient methods, hybrid, modifications of the standard scheme, memoryless BFGS preconditioned, and three-term. Other conjugate gradient methods with clustering the eigenvalues or with the minimization of the condition number of the iteration matrix, are also treated. For each method, the convergence analysis, the computational performances and the comparisons versus other conjugate gradient methods are given. The theory behind the conjugate gradient algorithms presented as a methodology is developed with a clear, rigorous, and friendly exposition; the reader will gain an understanding of their properties and their convergence and will learn to develop and prove the convergence of his/her own methods. Numerous numerical studies are supplied with comparisons and comments on the behavior of conjugate gradient algorithms for solving a collection of 800 unconstrained optimization problems of different structures and complexities with the number of variables in the range [1000,10000]. The book is addressed to all those interested in developing and using new advanced techniques for solving unconstrained optimization complex problems. Mathematical programming researchers, theoreticians and practitioners in operations research, practitioners in engineering and industry researchers, as well as graduate students in mathematics, Ph.D. and master students in mathematical programming, will find plenty of information and practical applications for solving large-scale unconstrained optimization problems and applications by conjugate gradient methods.
Author : Michael Bartholomew-Biggs
Release : 2008-12-16
Genre : Mathematics
Kind : eBook
Book Rating : 232/5 ( reviews)
Book Synopsis Nonlinear Optimization with Engineering Applications by : Michael Bartholomew-Biggs
Download or read book Nonlinear Optimization with Engineering Applications written by Michael Bartholomew-Biggs. This book was released on 2008-12-16. Available in PDF, EPUB and Kindle. Book excerpt: This textbook examines a broad range of problems in science and engineering, describing key numerical methods applied to real life. The case studies presented are in such areas as data fitting, vehicle route planning and optimal control, scheduling and resource allocation, sensitivity calculations and worst-case analysis. Chapters are self-contained with exercises provided at the end of most sections. Nonlinear Optimization with Engineering Applications is ideal for self-study and classroom use in engineering courses at the senior undergraduate or graduate level. The book will also appeal to postdocs and advanced researchers interested in the development and use of optimization algorithms.
Author : Anthony V. Fiacco
Release : 1964
Genre :
Kind : eBook
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Book Synopsis The Sequential Unconstrained Minimization Technique for Nonlinear Programming. Algorithm Ii. Optimum Gradients by Fibonacci Search by : Anthony V. Fiacco
Download or read book The Sequential Unconstrained Minimization Technique for Nonlinear Programming. Algorithm Ii. Optimum Gradients by Fibonacci Search written by Anthony V. Fiacco. This book was released on 1964. Available in PDF, EPUB and Kindle. Book excerpt: The algorithm has been revised to incorporate a more efficient technique for computing the minimum of a function of a specified vector, a computation required in each iteration of the optimumgradient method. The new technique is an adaptation of a Fibonacci-gradient method. The new technique is an adaptation of a Fibonacci previously used and results in a recuction in total problem solution time of almost one half. A new normalized final-convergence criterion that does not depend on the magnitude of the optimum solution value is given. The detailed computer solution of a change-constrained linear programming problem illustrates the typical convergence characteristics of the method. The remainder of the paper is a concise and simplified review of all the method's important computational aspects. (Author).
Author : Andrzej Ruszczynski
Release : 2011-09-19
Genre : Mathematics
Kind : eBook
Book Rating : 054/5 ( reviews)
Book Synopsis Nonlinear Optimization by : Andrzej Ruszczynski
Download or read book Nonlinear Optimization written by Andrzej Ruszczynski. This book was released on 2011-09-19. Available in PDF, EPUB and Kindle. Book excerpt: 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.
