7 edition of **Sparse matrix technology** found in the catalog.

- 143 Want to read
- 10 Currently reading

Published
**1984**
by Academic Press in London, Orlando
.

Written in English

- Sparse matrices -- Data processing.

**Edition Notes**

Statement | Sergio Pissanetzky. |

Classifications | |
---|---|

LC Classifications | QA188 .P57 1984 |

The Physical Object | |

Pagination | xiii, 321 p. : |

Number of Pages | 321 |

ID Numbers | |

Open Library | OL3193706M |

ISBN 10 | 0125575807 |

LC Control Number | 83073144 |

The book covers topics on storage schemes and computational techniques needed for sparse matrix technology; sparse matrix methods and algorithms for the direct solution of linear equations; and algorithms for different purposes connected with sparse matrix technology. Engineers, programmers, analysts, teachers, and students in the computer Author: Sergio Pissanetzky. Sparse matrix data structures Example: storage as compressed vector I Vectors x, y have length n = 8. I Their number of nonzeros is c x = 3 and c y = 4. I Acompressed vectordata structure for x and y is: x[j]:a = 2 5 1 x[j]:i = 5 3 7 y[j]:a = 1 4 1 4 y[j]:i = 6 3 5 2 I Here, the jth nonzero in File Size: 1MB.

Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery. The book gets you up to speed on the latest sparsity. Sparse Matrices A matrix is a two dimensional collection, typically thought of as having rows and columns of data. One of the easiest ways to create a matrix is to use a list of lists. For example, consider the matrix shown below. We can represent this collection as five rows, each row having five columns.

How to select features for 2D matrix data directly is still an uninvestigated important issue. In this paper, we propose an algorithm named sparse matrix regression (SMR) for direct feature selection on matrix data. It employs the matrix regression model to accept matrix as input and bridges each matrix Cited by: In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations.

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Sparse Matrix Technology: Pissanetzky, Sergio: : Books. Buy New. $ Qty: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Qty: by: The NOOK Book (eBook) of the Sparse Matrix Technology by Sergio Pissanetzky at Barnes & Noble.

FREE Shipping on $35 or more. Due to COVID, orders may be delayed. The text provides the fundamental methods, procedures, techniques, and applications of sparse matrix technology in software development. The book covers topics on storage schemes and computational techniques needed for sparse matrix technology; sparse matrix methods and algorithms for the direct solution of linear equations; and algorithms for different purposes connected with sparse matrix cturer: Academic Press.

Sparse Matrix Technology presents the methods, concepts, ideas, and applications of sparse matrix technology. The text provides the fundamental methods, procedures, techniques, and applications of sparse matrix technology in software development.

Sparse Matrix Technology presents the methods, concepts, ideas, and applications of sparse matrix technology. The text provides the fundamental methods, procedures, techniques, and applications of sparse matrix technology in software development.

The book covers topics on storage schemes and c. Sparse Matrix Technology presents the methods, concepts, ideas, and applications of sparse matrix technology.

The text provides the fundamental methods, procedures, techniques, and applications of sparse matrix technology in software development. The book covers topics on storage schemes and computational techniques needed for sparse matrix technology; sparse matrix methods and algorithms for the direct solution of linear equations; and algorithms for different purposes connected with sparse Book Edition: 1.

Sparse Matrix Technology - electronic edition. Sergio Pissanetzky. Academic Press, - pages. 1 Review. Preview this book Reviews: 1. The purpose of the book is to bring sparse matrix technology within reach of engineers, programmers, analysts, teachers and students.

Sparse Matrix Technology. Borrow eBooks, audiobooks, and videos from thousands of public libraries worldwide. Open Library is an open, editable library catalog, building towards a web page for every book ever published. Sparse Matrix Technology by Sergio Pissanetzky; 1 edition; First published in ; Subjects: Computer Technology, Mathematics, Nonfiction, Science.

This is an electronic edition of the classic Sparse Matrix Technology by Sergio Pissanetzky, originally published in English by Academic Press, London, inand translated into Russian and published by MIR, Moscow, in Sparse Matrix Technology by Pissanetzky, Sergio and Publisher Academic Press.

Save up to 80% by choosing the eTextbook option for ISBN:, The print version of this textbook is ISBN: ously. Sparse matrix technology is here and will stay. The concept expressing the nature of our concern is contained in the title of the book.

Technology is applied science, the science or study of the practical or industrial arts.7 The phrase "sparse matrix technology" was an. PDF | On Jan 1,Sergio Pissanetzky and others published Sparse Matrix Technology | Find, read and cite all the research you need on ResearchGateAuthor: Sergio Pissanetzky.

x CONTENTS Matrix-by-Vector Products The CSR and CSC Formats Matvecs in the. Additional Physical Format: Online version: Pissanetzky, Sergio. Sparse matrix technology. London ; Orlando: Academic Press, (OCoLC) Sparse Matrix Computations is a collection of papers presented at the Symposium by the same title, held at Argonne National Laboratory.

This book is composed of six parts encompassing 27 chapters that contain contributions in several areas of matrix computations and some of the most potential research in numerical linear Edition: 1.

Chapter 1 covers all fundamental material such as storage schemes, basic definitions and computational techniques needed for sparse matrix technology. It is very convenient to read at least Sections 1 to 9 and Section 12 of Chapter 1 first.

The first reading may, however, be superficial. The reader. Sparse Matrix Computations is a collection of papers presented at the Symposium by the same title, held at Argonne National Laboratory. This book is composed of six parts encompassing 27 chapters that contain contributions in several areas of matrix computations and some of the most potential research in numerical linear algebra.

Genre/Form: Electronic books: Additional Physical Format: Print version: Pissanetzky, Sergio. Sparse Matrix Technology. Burlington: Elsevier Science, ©. Handbook of Robust Low-Rank and Sparse Matrix Decomposition: Applications in Image and Video Processing shows you how robust subspace learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications.

Incorporating both existing and new ideas, the book conveniently gives you one-stop access to a number of Cited by: provides an overview of direct methods for sparse linear systems. Several of the early conference proceedings in the s and s on sparse matrix problems and algorithms have been published in book form, including Reid (), Rose and Willoughby (), Du (e), and Evans ().Cited by: A sparse matrix obtained when solving a finite element problem in two dimensions.

The non-zero elements are shown in black. In numerical analysis and scientific computing, a sparse matrix or sparse array is a matrix in which most of the elements are zero.