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Fall 2017
Sep 24, 2017
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MATH 560 - Optimization.
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

4.000 Credit hours

Schedule Types: Lecture, Final Exam, Midterm Exam

Faculty of Science
Mathematics and Statistics Department

Restrictions:
May not be enrolled in one of the following Faculties:     
      Faculty of Dentistry
      Faculty of Medicine
      School of Continuing Studies

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