Office hours: Tues 34pm in CSE 574
(or Zoom by appointment)
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 highdimensional optimization and sampling, and use them to understand the following concepts in detail:
Introduction  
Thu, Sep 30
Intro to convexity and optimization LV Book: 1.11.5 Homework #0 

Tue, Oct 05
Gradient descent, I LV Book: 2.12.3 

Thu, Oct 07
Gradient descent, II LV Book: 2.32.7 Homework #1 

Elimination  
Tue, Oct 12
Cutting plane methods, the Ellipsoid algorithm LV Book: 3.13.2 

Thu, Oct 14
NO CLASS 

Tue, Oct 19
From volume to function value LV Book: 3.23.3 

Thu, Oct 21
Center of gravity LV Book: 3.4 Homework #2 

Tue, Oct 26


Thu, Oct 28


Tue, Nov 02


Thu, Nov 04


Tue, Nov 09


Tue, Nov 16


Thu, Nov 18


Tue, Nov 23


Thu, Nov 25


Tue, Nov 30


Thu, Dec 02


Tue, Dec 07


Thu, Dec 09
