Material Detail
Lecture 13: Recap: Conjugate Gradient Method
This video was recorded at Stanford Engineering Everywhere EE364B - Convex Optimization II. 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
Quality
- User Rating
- Comments
- Learning Exercises
- Bookmark Collections
- Course ePortfolios
- Accessibility Info
More about this material
- Mobile Compatibility:
- Not specified at this time
- Language:
- English
- Cost Involved:
- No
- Source Code Available:
- No
- Creative Commons:
-
This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
