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The linearly constrained matrix rank minimization problem is widely applicable in many fields such as control, signal processing and system identification.
USSR Computational Mathematics and Mathematical Physics 7
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IEEE Journal on Selected Topics in Signal Processing 4
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Journal of Machine Learning Research 9
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Tech. rep., Caltech (2005)
Candès, E.J., Romberg, J.: ℓ 1 \ell_{1} -MAGIC: Recovery of sparse signals via convex programming · 2005
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SIAM MMS 4
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IEEE Transactions on Information Theory 52
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IEEE Transactions on Information Theory 52
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Cai, J., Candès, E.J., Shen, Z.: A singular value thresholding algorithm for matrix completion · 2008
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Submitted (2008)
Candès, E.J., Recht, B.: Exact matrix completion via convex optimization · 2008
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Preprint available at arXiv: 0803.0811 (2008)
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Candès, E.J., Tao, T.: The power of convex relaxation: near-optimal matrix completion · 2009
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Tech. rep., Department of IEOR, Columbia University (2009)
Goldfarb, D., Ma, S.: Convergence of fixed point continuation algorithms for matrix rank minimization · 2009
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Tech. rep., Department of IEOR, Columbia University (2009)
Wen, Z., Yin, W., Goldfarb, D., Zhang, Y.: A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation · 2009
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