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Apr 28, 2024
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2020-2022 Undergraduate and Graduate Bulletin (without addenda) [ARCHIVED CATALOG]
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MA-UY 3204 Linear and Nonlinear Optimization4 Credits
Optimization is a major part of the toolbox of the applied mathematician, and more broadly, of researchers in quantitative sciences including economics, data science, machine learning, and quantitative social sciences. The course provides an introduction to linear programming and convex optimization. It will cover some theory (duality, minimax problems, convexity) and algorithms (descent algorithms in the nonlinear case, simplex and interior point methods in the linear case). The course will put emphasis on numerical implementation (using Python/Numpy and Gurobi), as well on applications to economics (matching models, dynamic programming, resource allocation problems), and operations research (shortest path problems, and more general network flow problems).
Prerequisite(s): A grade of C or better in (MA-UY 2114 or MA-UY 2514 ) and (MA-UY 2034 or MA-UY 3034 or MA-UY 3044 or MA-UY 3054 )
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