Fundamentals of Matrix Computations
eBook - PDF

Fundamentals of Matrix Computations

  1. 335 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Fundamentals of Matrix Computations

About this book

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.

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Yes, you can access Fundamentals of Matrix Computations by Olga Moreira in PDF and/or ePUB format, as well as other popular books in Mathematics & Mathematics General. We have over one million books available in our catalogue for you to explore.

Information

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

Table of contents

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