MATH-UA 253 Linear and Nonlinear Optimization


4 points. Fall and Spring terms.

Course Description

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).

Prerequisites

MATH-UA 123 Calculus III OR MATH-UA 129 Honors Calculus III OR MATH-UA 213 Math for Economics III (for Economics majors) with a grade of C or better and/or the equivalent, AND MATH-UA 140 Linear Algebra OR MATH-UA 148 Honors Linear Algebra with a grade of C or better and/or the equivalent. 

Course Syllabi or Websites by Semester