Optimization Models and Applications
Instructors: A. Bayen
This course offers an introduction to optimization models and their applications, ranging from machine learning and statistics to decision-making and control, with emphasis on numerically tractable problems.
Students will gain familiarity with the mathematical machinery underlying convex optimization, including matrix-vector calculus and fundamental concepts of linear algebra.
The image below shows a graph of the Senators in the 2004-2006 US Senate, that is obtained by solving a specific optimization problem involving the estimation of covariance matrices with sparsity constraints. (For more details, see here.)
Tentative schedule: here.
Final exam: Wed 12/18/19, 8-11AM.