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Hands-on matrix algebra using R : active and motivated learning with applications / Hrishikesh D. Vinod.

By: Material type: TextTextPublication details: Singapore : World Scientific, ©2011.Description: xvii, 329 p. : ill. ; 23 cmISBN:
  • 9789814313698
Subject(s): DDC classification:
  • 512.9434 23 V788
Contents:
1. R preliminaries -- 2. Elementary geomentary geometry and algebra using R -- 3. Vector spaces -- 4. Matrix basics and R software -- 5. Decision applications, payoff matrix -- 6. Determinant and singularity of a square matrix -- 7. The norm, rank and trace of a matrix -- 8. Matrix inverse and solution of linear equations -- 9. Eigenvalues and eigenvectors -- 10. Similar matrices, quadratic and Jordan canonical forms -- 11. Hermitian, normal and positive define matrices -- 12. Kronecker products and singular value decomposition -- 13. Simultaneous reduction and vec stacking -- 14. Vector and matrix differentiation -- 15. Matrix results for statistics -- 16. Generalized inverse and patterned matrices -- 17. Numerical accuracy and QR decomposition.
Summary: Teaches matrix algebra, allowing the student to learn the material by actually working with matrix objects in modern computer environment of R. This book provides a comprehensive overview of matrix theory without being bogged down in proofs or tedium.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata 512.9434 V788 (Browse shelf(Opens below)) Available 137468
Total holds: 0

Includes bibliographical references and index.

1. R preliminaries --
2. Elementary geomentary geometry and algebra using R --
3. Vector spaces --
4. Matrix basics and R software --
5. Decision applications, payoff matrix --
6. Determinant and singularity of a square matrix --
7. The norm, rank and trace of a matrix --
8. Matrix inverse and solution of linear equations --
9. Eigenvalues and eigenvectors --
10. Similar matrices, quadratic and Jordan canonical forms --
11. Hermitian, normal and positive define matrices --
12. Kronecker products and singular value decomposition --
13. Simultaneous reduction and vec stacking --
14. Vector and matrix differentiation --
15. Matrix results for statistics --
16. Generalized inverse and patterned matrices --
17. Numerical accuracy and QR decomposition.

Teaches matrix algebra, allowing the student to learn the material by actually working with matrix objects in modern computer environment of R. This book provides a comprehensive overview of matrix theory without being bogged down in proofs or tedium.

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