Numerical Analysis and Optimization

Catalog description

This course covers Gaussian elimination, LU factorization, Cholesky decomposition, QR decomposition, Newton-Raphson method, binary search, convex function, convex set, gradient method, Newton method, Lagrange dual, KKT condition, interior point method, conjugate gradient method, random walk, and stochastic optimization. Students are not allowed to take both MATH 302 and MATH 304 because of the content overlap. Students who are planning to major in Applied Math and Computational Sciences should take MATH 302 instead, and those who have taken MATH 304 may not major in Applied Math and Computational Sciences. 001277