MATH344

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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)).

CEA: ProgCLA-CEA and Au Pair, EMMT: Major-Mathematics Elective, ENMH: Minor-Mathematics Elective, MMAT: Major-Mathematics, NMAT: Minor-Mathematics, ULVL: Upper Level UG Course