Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

YouTube

The SDP Relaxation for Max-Cut - Lecture 19b of CS Theory Toolkit

Ryan O'Donnell via YouTube

Overview

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only!
Explore the SDP relaxation technique for the Max-Cut problem in this graduate-level lecture from Carnegie Mellon University's "CS Theory Toolkit" course. Learn how to transform an exact quadratic program into a linear program with infinite constraints, and discover how this relaxation leads to a semidefinite program solvable by the Ellipsoid Algorithm. Delve into advanced topics in theoretical computer science, drawing from resources like "Geometric Algorithms and Combinatorial Optimization" and "Laplacian eigenvalues and the maximum cut problem." Taught by Professor Ryan O'Donnell, this 33-minute lecture covers key concepts including linear programming, the Ellipsoid Algorithm, and semidefinite programming, providing essential knowledge for research in theoretical computer science.

Syllabus

Intro
Linear Programming
Standard Linear Programming
Smart Idea
Ellipsoid Algorithm
Inequality
SDP
The LPE

Taught by

Ryan O'Donnell

Reviews

Start your review of The SDP Relaxation for Max-Cut - Lecture 19b of CS Theory Toolkit

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.