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1 - 1 of 1 results for: CME 336: Linear and Conic Optimization with Applications

CME 336: Linear and Conic Optimization with Applications (MS&E 314)

Linear, semidefinite, conic, and convex nonlinear optimization problems as generalizations of classical linear programming. Algorithms include the interior-point, barrier function, and cutting plane methods. Related convex analysis, including the separating hyperplane theorem, Farkas lemma, dual cones, optimality conditions, and conic inequalities. Complexity and/or computation efficiency analysis. Applications to combinatorial optimization, sensor network localization, support vector machine, and graph realization. Prerequisite: MS&E 211 or equivalent.
Terms: Win, alternate years, not given next year | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Ye, Y. (PI)
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