Date: Wed, 20 Nov 1996 22:11:30 GMT Server: NCSA/1.4.2 Content-type: text/html Last-modified: Tue, 03 Sep 1996 13:09:45 GMT Content-length: 1477 Numerical Linear Algebra

Numerical Linear Algebra

(Computer Science 106)

Identical to Engineering Sciences 104

Times: 98S: Arrange
Prerequisite: Computer Science 26, Mathematics 26, or Engineering Science 69. Students are to be familiar with approximation theory, error analysis, direct and iterative techniques for solving linear systems, and discretization of continuous problems to the level normally encountered in an undergraduate course in numerical analysis.

The course examines in the context of modern computational practice algorithms for solving linear systems Ax = b and Az = lx. Matrix decomposition algorithms, matrix inversion, and eigenvector expansions are studied. Algorithms for special matrix classes are featured, including symmetric positive definite matrices, banded matrices, and sparse matrices. Error analysis and complexity analysis of the algorithms are covered. The algorithms are implemented for selected examples chosen from elimination methods (linear systems), least squares (filters), linear programming, incidence matrices (networks and graphs), diagonalization (convolution), sparse matrices (partial differential equations).


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