  We use convex relaxation techniques to provide a sequence of solutions to the
matrix completion problem. Using the nuclear norm as a regularizer, we provide
simple and very efficient algorithms for minimizing the reconstruction error
subject to a bound on the nuclear norm. Our algorithm iteratively replaces the
missing elements with those obtained from a thresholded SVD. With warm starts
this allows us to efficiently compute an entire regularization path of
solutions.
