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GRADUATE COURSE IN MATRIX COMPUTATIONS FOR SPRING 1997 - MAT 767 [3 CREDITS]

It has been estimated that over 70% of computer models in science and
engineering rely in part at least on some type of linear algebraic
method. The impact of computational linear algebra is also being felt
in the analytical realm. The habits of the field reliance on
orthogonal matrices and the study of problem sensitivity under
perturbation have spilled over into diverse areas. A prime example is
the increased use of the singular value decomposition (SVD) as an
analytical tool in control engineering, sta tistics, signal
processing, image compression, and other disciplines. Another facet
of numerical linear algebra is that participants require skills from
both mathematics and computer science in order to provide effective
software tools. Moreover, the blossoming of new multiprocessor
architectures is making the field ever more pertinent and dynamic. The
course aims to give an overview of the field with focus on high-level
algorithmic concepts as they pertain to new computer
architectures. The presentation will be machine independent and at the
conceptual level in order to emphasize linear algebraic techniques
rather that real life applications on specific machines. However, some
small scale illustrative computing should be expected.

**Time:** Tuesday & Thursday 2:30-3:45 PM, Room: CBC C113

**Instructor:** Professor George Miel, Office: CBC B509, ph: 895-0360

**Text:** Gene H. Golub and Charles F. Van Loan, Matrix Computations, The John Hopkins University Press, second edition, 1989.

**Prerequisites:** Graduate standing and prior training in numerical analysis or consent of the instructor.

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