Sparse Solutions Of Underdetermined Linear Systems And Their Applications
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Author | : Ming-Jun Lai |
Publisher | : SIAM |
Total Pages | : |
Release | : 2021-06-25 |
Genre | : Mathematics |
ISBN | : 1611976510 |
This textbook presents a special solution to underdetermined linear systems where the number of nonzero entries in the solution is very small compared to the total number of entries. This is called a sparse solution. Since underdetermined linear systems can be very different, the authors explain how to compute a sparse solution using many approaches. Sparse Solutions of Underdetermined Linear Systems and Their Applications contains 64 algorithms for finding sparse solutions of underdetermined linear systems and their applications for matrix completion, graph clustering, and phase retrieval and provides a detailed explanation of these algorithms including derivations and convergence analysis. Exercises for each chapter help readers understand the material. This textbook is appropriate for graduate students in math and applied math, computer science, statistics, data science, and engineering. Advisors and postdoctoral scholars will also find the book interesting and useful.
Author | : Yaakov Tsaig |
Publisher | : |
Total Pages | : 154 |
Release | : 2007 |
Genre | : |
ISBN | : 9780549063803 |
Next, we introduce Stagewise Orthogonal Matching Pursuit (StOMP), a rapid iterative method to find sparse approximate solutions to underdetermined linear systems. We demonstrate that StOMP is much faster than competing approaches to recover sparse solutions, and at the same time, its ability to recover the sparsest solution is comparable with that of ℓ1 minimization.
Author | : Yousef Saad |
Publisher | : SIAM |
Total Pages | : 537 |
Release | : 2003-04-01 |
Genre | : Mathematics |
ISBN | : 0898715342 |
Mathematics of Computing -- General.
Author | : Timothy A. Davis |
Publisher | : SIAM |
Total Pages | : 228 |
Release | : 2006-09-01 |
Genre | : Computers |
ISBN | : 0898716136 |
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.
Author | : Andrew Gordon Wilcox |
Publisher | : |
Total Pages | : 80 |
Release | : 2010 |
Genre | : Algebras, Linear |
ISBN | : |
Author | : Amir Z. Averbuch |
Publisher | : |
Total Pages | : |
Release | : 2011 |
Genre | : Geology |
ISBN | : 9783642277931 |
Author | : Stephen Boyd |
Publisher | : Cambridge University Press |
Total Pages | : 477 |
Release | : 2018-06-07 |
Genre | : Business & Economics |
ISBN | : 1316518965 |
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.
Author | : Li︠u︡dmila Andreevna Pilipchuk |
Publisher | : |
Total Pages | : 235 |
Release | : 2013 |
Genre | : Algebra |
ISBN | : 9789855188736 |
Author | : David Leigh Donoho |
Publisher | : |
Total Pages | : 34 |
Release | : 2005 |
Genre | : |
ISBN | : |
Author | : M. Vidyasagar |
Publisher | : SIAM |
Total Pages | : 341 |
Release | : 2019-12-03 |
Genre | : Technology & Engineering |
ISBN | : 161197612X |
Compressed sensing is a relatively recent area of research that refers to the recovery of high-dimensional but low-complexity objects from a limited number of measurements. The topic has applications to signal/image processing and computer algorithms, and it draws from a variety of mathematical techniques such as graph theory, probability theory, linear algebra, and optimization. The author presents significant concepts never before discussed as well as new advances in the theory, providing an in-depth initiation to the field of compressed sensing. An Introduction to Compressed Sensing contains substantial material on graph theory and the design of binary measurement matrices, which is missing in recent texts despite being poised to play a key role in the future of compressed sensing theory. It also covers several new developments in the field and is the only book to thoroughly study the problem of matrix recovery. The book supplies relevant results alongside their proofs in a compact and streamlined presentation that is easy to navigate. The core audience for this book is engineers, computer scientists, and statisticians who are interested in compressed sensing. Professionals working in image processing, speech processing, or seismic signal processing will also find the book of interest.