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Overview
Mathematics & Statistics (Sci) : Line search methods including steepest descent, Newton's (and Quasi-Newton) methods. Trust region methods, conjugate gradient method, solving nonlinear equations, theory of constrained optimization including a rigorous derivation of Karush-Kuhn-Tucker conditions, convex optimization including duality and sensitivity. Interior point methods for linear programming, and conic programming.
Terms: Winter 2022
Instructors: Paquette, Courtney (Winter)
Prerequisite: Undergraduate background in analysis and linear algebra, with instructor's approval
