CSE 535: Theory of Optimization and Continuous Algorithms
The design of algorithms is traditionally a discrete endeavor. However, many advances have come from a continuous viewpoint. Typically, a continuous process, deterministic or randomized is designed (or shown) to have desirable properties, such as approaching an optimal solution or a desired distribution, and an algorithm is derived from this by appropriate discretization. In interesting and general settings, the current fastest methods are a consequence of this perspective. We will discuss several examples of algorithms for high-dimensional optimization and sampling, and use them to understand the following concepts in detail.
We hope that these concepts will help students become a designer instead of a consumer of continuous algorithms.
This course is offered in Georgia Tech at the same time by Santosh Vempala.
- Instructor: Yin Tat Lee
- Office Hours: By appointment, email me at firstname.lastname@example.org
- Lectures: Tue, Thu 10:00-11:20 at ARC G070
- Course evaluation: Homework (100%)
- Prerequisite: basic knowledge of algorithms, probability, linear algebra.
Submitted via Canvas.
- Assignment 1 due Sunday, 20-Jan 11:59PM
- Assignment 2 due Sunday, 3-Feb 11:59PM
- Assignment 3 due Sunday, 17-Feb 11:59PM
- Assignment 4 due Sunday, 3-Mar 11:59PM
- Assignment 5 due Sunday, 17-Mar 11:59PM
- Jan 08:
- Jan 10:
- Jan 15:
- Jan 17:
- Jan 22:
- Jan 24:
- Jan 29:
- Jan 31:
- Feb 05:
- Feb 07:
- Feb 12:
- Feb 14:
- Feb 19:
- Feb 21:
- Feb 26:
- Feb 28:
- Mar 5:
- Mar 7:
- Mar 12:
- Mar 14: