Lecture 13: Recap: Conjugate Gradient Method
published: July 21, 2010, recorded: April 2008, views: 3847
released under terms of: Creative Commons Attribution Non-Commercial (CC-BY-NC)
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
So we’re looking at solving symmetric positive definite systems of equations and this would come up in Newton’s method, it comes up in, you know, interior point methods, least squares, all these sorts of things. And last time we talked about, I mean, the CG Method the basic idea is it’s a method which solves Ax=b where A is positive definite. And – but it does so in a different way. ...
See the whole transcript at Convex Optimization II - Lecture 13
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !