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Author: Donghun Suh
Publisher:
ISBN:
Category : Conjugate gradient methods
Languages : en
Pages : 176
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Book Description
Author: Donghun Suh
Publisher:
ISBN:
Category : Conjugate gradient methods
Languages : en
Pages : 176
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Book Description
Author: M. J. D. Powell
Publisher:
ISBN:
Category : Algorithms
Languages : en
Pages : 24
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Book Description
Author: M. F. MacGuire
Publisher:
ISBN:
Category :
Languages : en
Pages : 11
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Book Description
Author: Neculai Andrei
Publisher: Springer Nature
ISBN: 3030429504
Category : Mathematics
Languages : en
Pages : 515
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Book Description
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: Josef Malek
Publisher: SIAM
ISBN: 161197383X
Category : Mathematics
Languages : en
Pages : 106
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Book Description
Preconditioning and the Conjugate Gradient Method in the Context of Solving PDEs?is about the interplay between modeling, analysis, discretization, matrix computation, and model reduction. The authors link PDE analysis, functional analysis, and calculus of variations with matrix iterative computation using Krylov subspace methods and address the challenges that arise during formulation of the mathematical model through to efficient numerical solution of the algebraic problem. The book?s central concept, preconditioning of the conjugate gradient method, is traditionally developed algebraically using the preconditioned finite-dimensional algebraic system. In this text, however, preconditioning is connected to the PDE analysis, and the infinite-dimensional formulation of the conjugate gradient method and its discretization and preconditioning are linked together. This text challenges commonly held views, addresses widespread misunderstandings, and formulates thought-provoking open questions for further research.?
Author: Martin Hanke
Publisher: CRC Press
ISBN: 1351458337
Category : Mathematics
Languages : en
Pages : 144
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Book Description
The conjugate gradient method is a powerful tool for the iterative solution of self-adjoint operator equations in Hilbert space.This volume summarizes and extends the developments of the past decade concerning the applicability of the conjugate gradient method (and some of its variants) to ill posed problems and their regularization. Such problems occur in applications from almost all natural and technical sciences, including astronomical and geophysical imaging, signal analysis, computerized tomography, inverse heat transfer problems, and many more This Research Note presents a unifying analysis of an entire family of conjugate gradient type methods. Most of the results are as yet unpublished, or obscured in the Russian literature. Beginning with the original results by Nemirovskii and others for minimal residual type methods, equally sharp convergence results are then derived with a different technique for the classical Hestenes-Stiefel algorithm. In the final chapter some of these results are extended to selfadjoint indefinite operator equations. The main tool for the analysis is the connection of conjugate gradient type methods to real orthogonal polynomials, and elementary properties of these polynomials. These prerequisites are provided in a first chapter. Applications to image reconstruction and inverse heat transfer problems are pointed out, and exemplarily numerical results are shown for these applications.
Author: William L. Briggs
Publisher: SIAM
ISBN: 9780898714623
Category : Mathematics
Languages : en
Pages : 318
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Book Description
Mathematics of Computing -- Numerical Analysis.
Author: E. Polak
Publisher: Academic Press
ISBN: 008096091X
Category : Business & Economics
Languages : en
Pages : 351
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Book Description
Computational Methods in Optimization
Author: Mary Catherine Hill
Publisher:
ISBN:
Category : Groundwater
Languages : en
Pages : 54
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Book Description
Author: Gerard Meurant
Publisher: SIAM
ISBN: 0898716160
Category : Computers
Languages : en
Pages : 374
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Book Description
The most comprehensive and up-to-date discussion available of the Lanczos and CG methods for computing eigenvalues and solving linear systems.
