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This course provides an elementary introduction to matrix theory and linear algebra with an emphasis on computation and applications. Topics include matrix algebra and linear systems; Gaussian elimination; LU and QR factorizations; determinants; vector spaces in R^n; the fundamental theorem of linear algebra; inner products, orthogonality, and least squares problems; eigenvalues, eigenvectors, and diagonalization; symmetric matrices and the spectral theorem; and singular value decomposition. Applications are drawn from graph theory, ranking and network analysis, image processing and compression, regression and data analysis, discrete dynamical systems, and mathematical modeling. MATLAB is used to support computation and visualization.

Prerequisite: ²Ñ´¡Ìý241 or ²Ñ´¡Ìý231; Corequisite: ²Ñ´¡Ìý341 (recommended)

Typically offered in Fall, Spring, and Summer