MATH344

Presents the mathematics behind how and why optimization algorithms work; finding maximum and minimum values of functions with and without constraints; convexity and concavity, the Hessian matrix, Newton-Raphson Method, classical unconstrained optimization (gradient methods, Newton’s Method), classical constrained optimization (Lagrange Multipliers, Kuhn-Tucker Theory), and linear algebra topics (orthogonality, orthogonal projections, Gram-Schmidt orthogonalization, QR factorization, least squares and weighted least squares, symmetric matrices and quadratic forms, singular value decomposition (SVD) and principal component analysis (PCA)).

Level: UG (I),