Solving a Low-Rank Factorization Model for Matrix Completion by a Non-linear Successive Over-Relaxation Algorithm

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dc.contributor.authorWen, Zaiwenen_US
dc.contributor.authorYin, Wotaoen_US
dc.contributor.authorZhang, Yinen_US
dc.date.accessioned2018-06-19T17:45:56Zen_US
dc.date.available2018-06-19T17:45:56Zen_US
dc.date.issued2010-03en_US
dc.date.noteMarch 2010en_US
dc.description.abstractThe 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.en_US
dc.format.extent24 ppen_US
dc.identifier.citationWen, Zaiwen, Yin, Wotao and Zhang, Yin. "Solving a Low-Rank Factorization Model for Matrix Completion by a Non-linear Successive Over-Relaxation Algorithm." (2010) <a href="https://hdl.handle.net/1911/102150">https://hdl.handle.net/1911/102150</a>.en_US
dc.identifier.digitalTR10-07en_US
dc.identifier.urihttps://hdl.handle.net/1911/102150en_US
dc.language.isoengen_US
dc.titleSolving a Low-Rank Factorization Model for Matrix Completion by a Non-linear Successive Over-Relaxation Algorithmen_US
dc.typeTechnical reporten_US
dc.type.dcmiTexten_US
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