Fundamentals of Matrix Computations

Olga Moreira

• Publisher: Arcler Press
• Year: 2019
• ISBN:
  9781774073773
• Topic: Mathematics
• Subtopic: Mathematics General
• Index: Mathematics

Fundamentals of Matrix Computations deals with the concept of matrix computations, a technique of singular value homogenization and its application in medical therapy. It consists of modern iterative methods to generalize the issues associated with singular-value homogenization. It provides the reader with the understanding of matrix computations and preconditioning technique of singular value homogenization so as to analyze its potential applications in the field of medical therapy and the use of efficient numerical methods so as to solve the problems linked with nonlinear singular boundary value by using improved differential transform method. This book also discusses about blind distributed estimation algorithms for adaptive networks, a dft-based approximate eigenvalue and singular value decomposition of polynomial matrices, sparse signal subspace decomposition based on adaptive over-complete dictionary, lower bounds for the low-rank matrix approximation and a semi-smoothing augmented lagrange multiplier algorithm for low-rank toeplitz matrix completion.

Table of contents

• Cover
• Title Page
• Copyright
• DECLARATION
• ABOUT THE EDITOR
• TABLE OF CONTENTS
• List of Contributors
• List of Abbreviations
• Preface
• Chapter 1 Singular Value Homogenization: a Simple Preconditioning Technique for Linearly Constrained Optimization and its Potential Applications in Medical Therapy
• Chapter 2 Perturbation Bounds for Eigenvalues of Diagonalizable Matrices and Singular Values
• Chapter 3 New Iterative Methods for Generalized Singular-value Problems
• Chapter 4 Blind Distributed Estimation Algorithms for Adaptive Networks
• Chapter 5 A DFT-based Approximate Eigenvalue and Singular Value Decomposition of Polynomial Matrices
• Chapter 6 Canonical Polyadic Decomposition of Third-order Semi-nonnegative Semi-symmetric Tensors using LU and QR Matrix Factorizations
• Chapter 7 Sparse Signal Subspace Decomposition based on Adaptive Over-complete Dictionary
• Chapter 8 Lower Bounds for the Low-rank Matrix Approximation
• Chapter 9 A Reduced-rank Approach for Implementing Higher-order Volterra Filters
• Chapter 10 A Semi-smoothing Augmented Lagrange Multiplier Algorithm for Low-rank Toeplitz Matrix Completion
• Chapter 11 Singular Spectrum-based MatrixCompletion for Time Series Recovery and Prediction
• Chapter 12 An Effective Numerical Method to Solve a Class of Nonlinear Singular Boundary Value Problems using improved Differential Transform Method
• Index
• Back Cover