Advanced Linear Algebra

Catalog description

Topics in linear algebra beyond those in a first course. For example: principal component analysis and other decompositions (singular value, Cholesky, etc.); Perron-Frobenius theory; positive semi-definite matrices; linear programming and more general convexity and optimization; basic simplicial topology; Gerschgorin theory; classical matrix groups. Applications to computer science, statistics, image processing, economics, or other fields of mathematics and science. Prerequisite: Mathematics 212 or 222 and Mathematics 218 or 221.