Fixed Point Iteration Algorithms for Low-rank Matrix Completion
Abstract
A lot of applications can be formulated as matrix completion problems. In order to
address such problems, a common assumption is that the underlying matrix is (approximately)
low-rank. Under certain conditions, the recovery of low-rank matrix can be done
via nuclear norm minimization, a convex program.
Scalable and fast algorithms are essential as the practical matrix completion tasks always
occur on a large scale. Here we study two algorithms and generalize the uni ed
framework of xed point iteration algorithm. We derive the convergence results and propose
a new algorithm based on the insights. Compared with the baseline algorithms, our
proposed method is signi cantly more e cient without loss of precision and acceleration
potentiality.
iii
Collections
Cite this version of the work
Xingliang Huang
(2015).
Fixed Point Iteration Algorithms for Low-rank Matrix Completion. UWSpace.
http://hdl.handle.net/10012/9370
Other formats