Algorithms For Solving Overdetermined Systems Of Linear Equations In The L P Subsript Sense
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Algorithms For Solving Overdetermined Systems Of Linear Equations In The L P Subsript Sense
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Author : Robert William Owens
language : en
Publisher:
Release Date : 1975
Algorithms For Solving Overdetermined Systems Of Linear Equations In The L P Subsript Sense written by Robert William Owens and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1975 with Algebras, Linear categories.
Iterative Methods For Sparse Linear Systems
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Author : Yousef Saad
language : en
Publisher: SIAM
Release Date : 2003-04-01
Iterative Methods For Sparse Linear Systems written by Yousef Saad and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-04-01 with Mathematics categories.
Mathematics of Computing -- General.
Numerical Algorithms
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Author : Justin Solomon
language : en
Publisher: CRC Press
Release Date : 2015-06-24
Numerical Algorithms written by Justin Solomon and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-24 with Computers categories.
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig
Subspace Identification For Linear Systems
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Author : Peter van Overschee
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Subspace Identification For Linear Systems written by Peter van Overschee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with theapplication of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering.
Direct Methods For Sparse Linear Systems
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Author : Timothy A. Davis
language : en
Publisher: SIAM
Release Date : 2006-09-01
Direct Methods For Sparse Linear Systems written by Timothy A. Davis and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-01 with Computers categories.
The sparse backslash book. Everything you wanted to know but never dared to ask about modern direct linear solvers. Chen Greif, Assistant Professor, Department of Computer Science, University of British Columbia.Overall, the book is magnificent. It fills a long-felt need for an accessible textbook on modern sparse direct methods. Its choice of scope is excellent John Gilbert, Professor, Department of Computer Science, University of California, Santa Barbara.Computational scientists often encounter problems requiring the solution of sparse systems of linear equations. Attacking these problems efficiently requires an in-depth knowledge of the underlying theory, algorithms, and data structures found in sparse matrix software libraries. Here, Davis presents the fundamentals of sparse matrix algorithms to provide the requisite background. The book includes CSparse, a concise downloadable sparse matrix package that illustrates the algorithms and theorems presented in the book and equips readers with the tools necessary to understand larger and more complex software packages.With a strong emphasis on MATLAB and the C programming language, Direct Methods for Sparse Linear Systems equips readers with the working knowledge required to use sparse solver packages and write code to interface applications to those packages. The book also explains how MATLAB performs its sparse matrix computations.Audience This invaluable book is essential to computational scientists and software developers who want to understand the theory and algorithms behind modern techniques used to solve large sparse linear systems. The book also serves as an excellent practical resource for students with an interest in combinatorial scientific computing.Preface; Chapter 1: Introduction; Chapter 2: Basic algorithms; Chapter 3: Solving triangular systems; Chapter 4: Cholesky factorization; Chapter 5: Orthogonal methods; Chapter 6: LU factorization; Chapter 7: Fill-reducing orderings; Chapter 8: Solving sparse linear systems; Chapter 9: CSparse; Chapter 10: Sparse matrices in MATLAB; Appendix: Basics of the C programming language; Bibliography; Index.
An Algorithm For Solving Overdetermined Linear Equations In The Lp Sense
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Author : Charles S. Duris
language : en
Publisher:
Release Date : 1970*
An Algorithm For Solving Overdetermined Linear Equations In The Lp Sense written by Charles S. Duris and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1970* with Algebras, Linear categories.
Mathematical Methods And Algorithms For Signal Processing
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Author : Todd K. Moon
language : en
Publisher: Pearson
Release Date : 2000
Mathematical Methods And Algorithms For Signal Processing written by Todd K. Moon and has been published by Pearson this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000 with Computers categories.
This previously included a CD. The CD contents can be accessed via World Wide Web.
Applied Numerical Methods Using Matlab
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Author : Won Y. Yang
language : en
Publisher: John Wiley & Sons
Release Date : 2005-05-20
Applied Numerical Methods Using Matlab written by Won Y. Yang and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-05-20 with Mathematics categories.
In recent years, with the introduction of new media products, there has been a shift in the use of programming languages from FORTRAN or C to MATLAB for implementing numerical methods. This book makes use of the powerful MATLAB software to avoid complex derivations, and to teach the fundamental concepts using the software to solve practical problems. Over the years, many textbooks have been written on the subject of numerical methods. Based on their course experience, the authors use a more practical approach and link every method to real engineering and/or science problems. The main benefit is that engineers don't have to know the mathematical theory in order to apply the numerical methods for solving their real-life problems. An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.
Computational Differential Equations
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Author : Kenneth Eriksson
language : en
Publisher: Cambridge University Press
Release Date : 1996-09-05
Computational Differential Equations written by Kenneth Eriksson and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-09-05 with Mathematics categories.
This textbook on computational mathematics is based on a fusion of mathematical analysis, numerical computation and applications.
Practical Optimization Methods
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Author : M. Asghar Bhatti
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Practical Optimization Methods written by M. Asghar Bhatti and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
The goal of this book is to present basic optimization theory and modern computational algorithms in a concise manner. The book is suitable for un dergraduate and graduate students in all branches of engineering, operations research, and management information systems. The book should also be use ful for practitioners who are interested in learning optimization and using these techniques on their own. Most available books in the field tend to be either too theoretical or present computational algorithms in a cookbook style. An approach that falls some where in between these two extremes is adopted in this book. Theory is pre sented in an informal style to make sense to most undergraduate and graduate students in engineering and business. Computational algorithms are also de veloped in an informal style by appealing to readers' intuition rather than mathematical rigor. The available, computationally oriented books generally present algorithms alone and expect readers to perform computations by hand or implement these algorithms by themselves. This obviously is unrealistic for a usual introductory optimization course in which a wide variety of optimization algorithms are discussed. There are some books that present programs written in traditional computer languages such as Basic, FORTRAN, or Pascal. These programs help with computations, but are of limited value in developing understanding of the algorithms because very little information about the intermediate steps v ' Preface VI -------------------------------------------------------- is presented.