Lecture 13: Recap: Conjugate Gradient Method

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 [[http://see.stanford.edu/materials/lsocoee364b/transcripts/ConvexOptimizationII-Lecture13.pdf|Convex Optimization II - Lecture 13]]