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Fast Solvers for Mesh-Based Computations

sparse matrices. Each chapter explains how to design and implement a parallel sparse direct solver specific for a particular structure of the;

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High Performance Computing for Computational Science -- VECPAR 2014

applications, numerical algorithms, direct/hybrid methods for solving sparse matrices, performance tuning. The volume also contains the papers;

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Direct Methods for Sparse Matrices

solved were it not for the fact that the matrices contain relatively few non-zero entries. Only comparatively recently, in the last fifteen years;

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Exploiting Hidden Structure in Matrix Computations: Algorithms and Applications

of efficient numerical methods, for example algorithms for matrices with low-rank block structure, matrices with decay, and structured tensor;

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High-Dimensional Covariance Estimation

Methods for estimating sparse and large covariance matrices Covariance and correlation matrices play fundamental roles in every aspect;

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Numerical Linear Algebra

This book combines a solid theoretical background in linear algebra with practical algorithms for numerical solution of linear algebra;

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Harry Markowitz

, sparse matrices and the SIMSCRIPT computer language. While Dr Markowitz is well-known for his work on portfolio theory, his work on sparse;

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Iterative Solution of Large Sparse Systems of Equations

ignored in other literature. The necessary amount of work increases dramatically with the size of systems, so one has to search for algorithms;

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Handbook of Robust Low-Rank and Sparse Matrix Decomposition

learning and tracking by decomposition into low-rank and sparse matrices provide a suitable framework for computer vision applications;

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Sparse representation of High Dimensional Data for Classification

In this book you will find the use of sparse Principal Component Analysis (PCA) for representing high dimensional data for classification;

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Cell-Derived Matrices Part B157

-dimensional cell-derived matrices for tissue engineering applications. Chapters in this new release include Glaucomatous cell-derived matrices;

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Bayesian Deconvolution of Sparse Processes

Bayesian Deconvolution of Sparse Processes: In this work, various Bayesian methods for deconvolution and blind deconvolution of sparse;

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Computational Methods in Physics

retrospective and recovery problems for partial differential equations. The revised text now includes an introduction to sparse matrix methods, the;

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Computational Methods in Physics

retrospective and recovery problems for partial differential equations. The revised text now includes an introduction to sparse matrix methods, the;

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Numerical Linear Algebra for High-Performance Computers

development, and software for direct solution of dense matrix problems, direct solution of sparse systems of equations, iterative solution of sparse;

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Sparse Optimization Theory and Methods

as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical;

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Sparse Optimization Theory and Methods

as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical;

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Cell-derived Matrices Part A

-dimensional cell-derived matrices for tissue engineering applications. Biochemical, biophysical and cell biological approaches are presented, along;

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Matrix Computations and Semiseparable Matrices - Eigenvalve and Singular Value Methods V2

value computations for semiseparable matrices. These matrices have hidden properties that allow the development of efficient methods and;

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Image Understanding Using Sparse Representations

theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms;

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Quaternion Matrix Computations

, especially the LU, the Cholesky, the QR and the singular value decomposition of quaternion matrices, direct and iterative methods for solving;

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High Performance Algorithms for Structured Matrix Problems

solution techniques for dense Toeplitz and Hankel matrices. The papers also discuss implementation issues on numerous parallel architectures such;

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Fundamentals of Matrix Computations

algorithms for adaptive networks, a dft-based approximate eigenvalue and singular value decomposition of polynomial matrices, sparse signal subspace;

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Iterative Methods and Preconditioning for Large and Sparse Linear Systems with Applications

This book describes, in a basic way, the most useful and effective iterative solvers and appropriate preconditioning techniques for some;

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Numerical Linear Algebra: An Introduction

standard material such as direct methods for solving linear systems and least-squares problems, error, stability and conditioning, basic iterative;

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Numerical Linear Algebra

standard material such as direct methods for solving linear systems and least-squares problems, error, stability and conditioning, basic iterative;

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Matrix Preconditioning Techniques and Applications

Preconditioning techniques have emerged as an essential part of successful and efficient iterative solutions of matrices. Ke Chen's book;

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