This is an introductory course on optimization that will cover the rudiments of unconstrained and constrained optimization of a real-valued multivariate function. The focus is on the settings where this function is, respectively, linear, quadratic, convex, or differentiable. Time permitting, topics such as nonsmooth, integer, vector, and dynamic optimization may be briefly addressed. Materials will include basic duality theory, optimality conditions, and intractability results, as well as algorithms and applications.
Location: Lectures held online through Canvas
Times: Mon & Wed, 4:10–5:30pm
Office: Jones 122C
Tel: (773) 702-4263
Office hours: Held online through Canvas, Tue, 9:00–10:30am.
Course Assistant I:
Office hours: Held online through Canvas, Fri, 4:00–5:30am.
Course Assistant II:
Office hours: Held online through Canvas, Thu, 9:00–10:30am.
Collaborations are permitted but you will need to write up your own solutions and declare your collaborators. The problem sets are designed to get progressively more difficult. You will get at least six days for each problem set.
You are required to implement your own programs for problems that require some amount of simple coding (using Matlab, Mathematica, R, or SciPy).
Bug report on the problem sets: lekheng(at)uchicago.edu
Grade composition: Option 1 – Four problem sets each counting towards 25% of grade; Option 2 – Four problem sets and one final exam each counting towards 20% of grade.
We will not use any specific textbook but will use selected material from the following references, all of which would be accessible to undergraduates.
You may download all these books online from an UChicago IP address or via ProxyIt! if you are off-campus.