Description
The course examines, in the context of modern computational practice, algorithms
for solving linear systems Ax = b and Ax = λx. 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),
Prerequisites
COSC 71 or ENGS 91. Students are to be familiar with approximation theory, error analysis, direct and iterative technique for solving linear systems, and discretization of continuous problems to the level normally encountered in an undergraduate course in
numerical analysis.
Cross Listed Courses
COSC 271
Notes
Not offered 2021-2023
