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- Strang: Introduction to Linear Algebra: good, chatty book.
- Strang: Linear Algebra with Applications: slightly more comprehensive.
- Richard Hill: Linear Algebra: computationally oriented, very good.
- David C. Lay: slightly simpler to read than Hill's book, again very good.
- Horn and Johnston: Matrix Analysis (Cambridge University Press). This is a very advanced book, but very readable, and it contains everything that anyone could possibly want to know about
the theory of linear algebra; it is computationally or algorithmically
aware, and so oriented, but it is a work on the mathematics. An
- Numerical Linear Algebra:
- W. W. Hager: Applied Numerical Linear Algebra.
QA 184 H33 (someone has it out now, though).
Good book, especially on eigenvalue computations.
- Golub and Van Loan: Matrix Computations. This is the
`bible' for numerical linear algebraists, though Stewart's book
is also excellent. This book contains many useful algorithms in an easy-to-program format, and a lot of useful analysis.
- LAPACK user's guide, 2nd ed. Good explanations of backward
error analysis and the LAPACK philosophy, as well as useful reference
to how to use the LAPACK routines.
- Linear Algebra with Maple: Bauldry, Evans, and Johnson.
This is at least an ok book (I haven't looked at it at all carefully,
yet). It does contain useful Maple commands and examples and discussions and exercises.
- Books to avoid: Anton (which defines eigenvalues as being
real (!)) and Auel (which is gung-ho for Maple at the expense of
readability or even common sense).
Wed Jan 31 11:33:59 EST 1996