Optimization. - 17049 - MATH 560 - 001 | ||||||||||||
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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.
Prerequisite: Undergraduate background in analysis and linear algebra, with instructor's approval Associated Term: Winter 2021 Registration Dates: Apr 01, 2020 to Jan 22, 2021 Lecture Schedule Type 4.000 Credits View Catalog Entry
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