Show simple item record

dc.contributor.authorWen, Zaiwen
Yin, Wotao
Zhang, Yin
dc.date.accessioned 2018-06-19T17:45:56Z
dc.date.available 2018-06-19T17:45:56Z
dc.date.issued 2010-03
dc.identifier.citation Wen, Zaiwen, Yin, Wotao and Zhang, Yin. "Solving a Low-Rank Factorization Model for Matrix Completion by a Non-linear Successive Over-Relaxation Algorithm." (2010) https://hdl.handle.net/1911/102150.
dc.identifier.urihttps://hdl.handle.net/1911/102150
dc.description.abstract The matrix completion problem is to recover a low-rank matrix from a subset of its entries. The main solution strategy for this problem has been based on nuclear-norm minimization which requires computing singular value decompositions -- a task that is increasingly costly as matrix sizes and ranks increase. To improve the capacity of solving large-scale problems, we propose a low-rank factorization model and construct a nonlinear successive over-relaxation (SOR) algorithm that only requires solving a linear least squares problem per iteration. Convergence of this nonlinear SOR algorithm is analyzed. Numerical results show that the algorithm can reliably solve a wide range of problems at a speed at least several times faster than nuclear-norm minimization algorithms.
dc.format.extent 24 pp
dc.title Solving a Low-Rank Factorization Model for Matrix Completion by a Non-linear Successive Over-Relaxation Algorithm
dc.type Technical report
dc.date.note March 2010
dc.identifier.digital TR10-07
dc.type.dcmi Text


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record