Sketching as a Tool for Numerical Linear Algebra PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Sketching as a Tool for Numerical Linear Algebra PDF full book. Access full book title Sketching as a Tool for Numerical Linear Algebra by David P. Woodruff. Download full books in PDF and EPUB format.
Author: David P. Woodruff
Publisher: Now Publishers
ISBN: 9781680830040
Category : Computers
Languages : en
Pages : 168
Get Book Here
Book Description
Sketching as a Tool for Numerical Linear Algebra highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given a matrix, one first compressed it to a much smaller matrix by multiplying it by a (usually) random matrix with certain properties. Much of the expensive computation can then be performed on the smaller matrix, thereby accelerating the solution for the original problem. It is an ideal primer for researchers and students of theoretical computer science interested in how sketching techniques can be used to speed up numerical linear algebra applications.
Author: David P. Woodruff
Publisher: Now Publishers
ISBN: 9781680830040
Category : Computers
Languages : en
Pages : 168
Get Book Here
Book Description
Sketching as a Tool for Numerical Linear Algebra highlights the recent advances in algorithms for numerical linear algebra that have come from the technique of linear sketching, whereby given a matrix, one first compressed it to a much smaller matrix by multiplying it by a (usually) random matrix with certain properties. Much of the expensive computation can then be performed on the smaller matrix, thereby accelerating the solution for the original problem. It is an ideal primer for researchers and students of theoretical computer science interested in how sketching techniques can be used to speed up numerical linear algebra applications.
Author: René van Bevern
Publisher: Springer
ISBN: 303019955X
Category : Computers
Languages : en
Pages : 397
Get Book Here
Book Description
This book constitutes the proceedings of the 14th International Computer Science Symposium in Russia, CSR 2019, held in Novosibirsk, Russia, in July 2019. The 31 full papers were carefully reviewed and selected from 71 submissions. The papers cover a wide range of topics such as algorithms and data structures; computational complexity; randomness in computing; approximation algorithms; combinatorial optimization; constraint satisfaction; computational geometry; formal languages and automata; codes and cryptography; combinatorics in computer science; applications of logic to computer science; proof complexity; fundamentals of machine learning; and theoretical aspects of big data.
Author:
Publisher: Springer Nature
ISBN: 3031743709
Category :
Languages : en
Pages : 444
Get Book Here
Book Description
Author: Justin Solomon
Publisher: CRC Press
ISBN: 1482251892
Category : Computers
Languages : en
Pages : 400
Get Book Here
Book Description
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
Author: Ilse C. F. Ipsen
Publisher: SIAM
ISBN: 0898716764
Category : Mathematics
Languages : en
Pages : 135
Get Book Here
Book Description
Matrix analysis presented in the context of numerical computation at a basic level.
Author: Michael W. Mahoney
Publisher: American Mathematical Soc.
ISBN: 1470435756
Category : Computers
Languages : en
Pages : 340
Get Book Here
Book Description
Nothing provided
Author: Maolin Che
Publisher: Springer Nature
ISBN: 9811520593
Category : Mathematics
Languages : en
Pages : 258
Get Book Here
Book Description
The book provides an introduction of very recent results about the tensors and mainly focuses on the authors' work and perspective. A systematic description about how to extend the numerical linear algebra to the numerical multi-linear algebra is also delivered in this book. The authors design the neural network model for the computation of the rank-one approximation of real tensors, a normalization algorithm to convert some nonnegative tensors to plane stochastic tensors and a probabilistic algorithm for locating a positive diagonal in a nonnegative tensors, adaptive randomized algorithms for computing the approximate tensor decompositions, and the QR type method for computing U-eigenpairs of complex tensors. This book could be used for the Graduate course, such as Introduction to Tensor. Researchers may also find it helpful as a reference in tensor research.
Author: Per-Gunnar Martinsson
Publisher: SIAM
ISBN: 1611976049
Category : Mathematics
Languages : en
Pages : 332
Get Book Here
Book Description
Fast solvers for elliptic PDEs form a pillar of scientific computing. They enable detailed and accurate simulations of electromagnetic fields, fluid flows, biochemical processes, and much more. This textbook provides an introduction to fast solvers from the point of view of integral equation formulations, which lead to unparalleled accuracy and speed in many applications. The focus is on fast algorithms for handling dense matrices that arise in the discretization of integral operators, such as the fast multipole method and fast direct solvers. While the emphasis is on techniques for dense matrices, the text also describes how similar techniques give rise to linear complexity algorithms for computing the inverse or the LU factorization of a sparse matrix resulting from the direct discretization of an elliptic PDE. This is the first textbook to detail the active field of fast direct solvers, introducing readers to modern linear algebraic techniques for accelerating computations, such as randomized algorithms, interpolative decompositions, and data-sparse hierarchical matrix representations. Written with an emphasis on mathematical intuition rather than theoretical details, it is richly illustrated and provides pseudocode for all key techniques. Fast Direct Solvers for Elliptic PDEs is appropriate for graduate students in applied mathematics and scientific computing, engineers and scientists looking for an accessible introduction to integral equation methods and fast solvers, and researchers in computational mathematics who want to quickly catch up on recent advances in randomized algorithms and techniques for working with data-sparse matrices.
Author: Miguel R. D. Rodrigues
Publisher: Cambridge University Press
ISBN: 1108427138
Category : Computers
Languages : en
Pages : 561
Get Book Here
Book Description
The first unified treatment of the interface between information theory and emerging topics in data science, written in a clear, tutorial style. Covering topics such as data acquisition, representation, analysis, and communication, it is ideal for graduate students and researchers in information theory, signal processing, and machine learning.
Author: Yipeng Liu
Publisher: Springer Nature
ISBN: 3030743861
Category : Technology & Engineering
Languages : en
Pages : 347
Get Book Here
Book Description
Tensor is a natural representation for multi-dimensional data, and tensor computation can avoid possible multi-linear data structure loss in classical matrix computation-based data analysis. This book is intended to provide non-specialists an overall understanding of tensor computation and its applications in data analysis, and benefits researchers, engineers, and students with theoretical, computational, technical and experimental details. It presents a systematic and up-to-date overview of tensor decompositions from the engineer's point of view, and comprehensive coverage of tensor computation based data analysis techniques. In addition, some practical examples in machine learning, signal processing, data mining, computer vision, remote sensing, and biomedical engineering are also presented for easy understanding and implementation. These data analysis techniques may be further applied in other applications on neuroscience, communication, psychometrics, chemometrics, biometrics, quantum physics, quantum chemistry, etc. The discussion begins with basic coverage of notations, preliminary operations in tensor computations, main tensor decompositions and their properties. Based on them, a series of tensor-based data analysis techniques are presented as the tensor extensions of their classical matrix counterparts, including tensor dictionary learning, low rank tensor recovery, tensor completion, coupled tensor analysis, robust principal tensor component analysis, tensor regression, logistical tensor regression, support tensor machine, multilinear discriminate analysis, tensor subspace clustering, tensor-based deep learning, tensor graphical model and tensor sketch. The discussion also includes a number of typical applications with experimental results, such as image reconstruction, image enhancement, data fusion, signal recovery, recommendation system, knowledge graph acquisition, traffic flow prediction, link prediction, environmental prediction, weather forecasting, background extraction, human pose estimation, cognitive state classification from fMRI, infrared small target detection, heterogeneous information networks clustering, multi-view image clustering, and deep neural network compression.
