In this book the authors try to bridge the gap between the treatments of matrix theory and linear algebra. It is aimed at graduate and advanced undergraduate students seeking a foundation in mathematics, computer science, or engineering. It will also be useful as a reference book for those working on matrices and linear algebra for use in their scientific work.
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Most Helpful Customer Reviews
3 of 3 people found the following review helpful:
5.0 out of 5 stars
Lancaster and Tismenetsky,
By gregory.e.coxson (Moorestown, NJ United States) - See all my reviews
This review is from: The Theory of Matrices, Second Edition: With Applications (Computer Science and Scientific Computing) (Hardcover)
As a graduate student, I had this book for a graduate course in Matrix Theory, taught by Dr. Hans Schneider at the University of Wisconsin-Madison. I've always liked it. Now that I'm planning my own course in Matrix Theory, I've been searching for just the right book for the type of course I want, which is an intro graduate level course for working engineers. I have tried, but cannot find, and don't really need, anything other than this book.
1 of 1 people found the following review helpful:
5.0 out of 5 stars
one marvelous point of view,
By
This review is from: The Theory of Matrices, Second Edition: With Applications (Computer Science and Scientific Computing) (Hardcover)
What I consider "The Great general books on Linear Algebra" (this book, the Horn/Johnson ones, and the "evergreen" Gantmacher) each have a different point of view on this subject. I learned my first advanced Matrix theory on this book, and the "space transformation" oriented approach here displayed is essential for geometry oriented minds. (yes...unfortunately this book is still a bit expensive... )
4 of 8 people found the following review helpful:
3.0 out of 5 stars
better books out there,
By A Customer
This review is from: The Theory of Matrices, Second Edition: With Applications (Computer Science and Scientific Computing) (Hardcover)
I would rather recomment Horn and Johnson's Matrix Analysis,since their book is much better.
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Inside This Book
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Key Phrases - Statistically Improbable Phrases (SIPs):
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monic matrix polynomials, same invariant polynomials, invertible leading coefficient, annihilating polynomial, orthonormal eigenbasis, linear elementary divisors, natural normal form, matrix norm induced, pure imaginary zeros, extremal vector, best approximate solution, standard triple, unitary space, linear transformation acting, norm axioms, leading principal minors, rank decomposition, leading principal submatrix, controllable subspace, singular bases, scalar polynomials, perturbed eigenvalues, companion matrix, latent vectors, component matrices Key Phrases - Capitalized Phrases (CAPs): (learn more)
Ker Zko, Matrix Let, Hence Theorem New!
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monic matrix polynomials, same invariant polynomials, invertible leading coefficient, annihilating polynomial, orthonormal eigenbasis, linear elementary divisors, natural normal form, matrix norm induced, pure imaginary zeros, extremal vector, best approximate solution, standard triple, unitary space, linear transformation acting, norm axioms, leading principal minors, rank decomposition, leading principal submatrix, controllable subspace, singular bases, scalar polynomials, perturbed eigenvalues, companion matrix, latent vectors, component matrices Key Phrases - Capitalized Phrases (CAPs): (learn more)
Ker Zko, Matrix Let, Hence Theorem New!
Books on Related Topics | Concordance | Text Stats Browse Sample Pages:
Front Cover | Table of Contents | First Pages | Index | Back Cover | Surprise Me!
Citations (learn more)
This book cites 23
books:
See all 23 books this book cites
2
books
cite this book:
- Numerical Analysis (Proceedings of Symposia in Applied Mathematics, V. 22) by American Mathematical Society in Back Matter
- Matrix Iterative Analysis (Springer Series in Computational Mathematics) by Richard S. Varga in Back Matter
- Perturbation Theory for Linear Operators (Classics in Mathematics) by Tosio Kato in Back Matter
- A Survey of Matrix Theory and Matrix Inequalities (Dover Books on Mathematics) by Marvin Marcus in Back Matter
- Linear Multivariable Control: A Geometric Approach (Stochastic Modelling and Applied Probability) by W. M. Wonham in Back Matter
See all 23 books this book cites
- Optimum Array Processing (Detection, Estimation, and Modulation Theory, Part IV) by Harry L. Van Trees on page 1405
- Numerical Methods of Statistics (Cambridge Series in Statistical and Probabilistic Mathematics) by John F. Monahan on page 60
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