Title: Parallel GPU implementation of null space based alternating optimization algorithm for large-scale matrix rank minimization
Abstract: This paper provides an alternating optimization algorithm for large-scale matrix rank minimization problems and its parallel implementation on GPU. The matrix rank minimization problem has a lot of important applications in signal processing, and several useful algorithms have been proposed. However most algorithms cannot be applied to a large-scale problem because of high computational cost. This paper proposes a null space based algorithm, which provides a low-rank solution without computing inverse matrix nor singular value decomposition. The algorithm can be parallelized easily without any approximation and can be applied to a large-scale problem. Numerical examples show that the algorithm provides a low-rank solution efficiently and can be speed up by parallel GPU computing.
Publication Year: 2012
Publication Date: 2012-03-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 14
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