AM221 : Advanced Optimization

Logistics

Instructor:

Rasmus Kyng (OH: Monday 4:00–5:00pm, MD’s 2nd floor lounge)

Time:

Monday & Wednesday, 2:30pm–4:00pm

Room:

60 Oxford St, Room 330

Teaching fellow:

Ariel Herbert-Voss (OH: Tuesday 5:30pm-6:30 pm. Room: MD 2nd floor lounge)

Teaching fellow:

Zhao Song (OH: Tuesday 7:30pm-8:30 pm. Room: MD 2nd floor lounge)

Grader:

Yijun Shen

Section (Ariel):

Friday, 11:30am-12:30pm. Room: MD 223

Section (Zhao):

Friday, 9:30am-10:30am. Room: MD 119 (Grace Murray Hopper Conference Room)

Contact:

Please use the Inbox in Canvas

Overview

This is a graduate level course on optimization which provides a foundation for applications such as statistical machine learning, signal processing, finance, and approximation algorithms. The course will cover fundamental concepts in optimization theory, modeling, and algorithmic techniques for solving large-scale optimization problems. Topics include elements of convex analysis, linear programming, Lagrangian duality, optimality conditions, and discrete and combinatorial optimization. Exercises and the class project will involve developing and implementing optimization algorithms.

Previous Years

Prof. Yaron Singer has taught this course twice before, in Spring 2014 and Spring 2016. For most of the course, we will follow the Spring 2016 syllabus closely, but at the end we will focus more on convex optimization, and spend a little less time on submodular optimization.