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Non-convex constraints have recently proven a valuable tool in many optimisation problems.
Regularization of Inverse Problems
H. W. Engl, M. Hanke, and A. Neubauer, · 2000
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“For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution,”
D.L. Donoho, · 2006
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“An introduction to compressive sampling,”
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E. Candès, “The restricted isometry property and its implications for compressed sensing.,” Compte Rendus de l’Academie des Sciences
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D. Needell and J. Tropp, “COSAMP: Iterative signal reovery from incomplete and inacurate samples.,” Appl. Comp. Harmonic Anal
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W. Dai and O. Milenkovic, “Subspace pursuit for compressed sensing: Closing the gap between performance and complexity,” IEEE Transactions on Information Theory
2009
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“Iterative Hard Thresholding for Compressed Sensing,”
T. Blumensath and M.E. Davies · 2009
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“Blind Multi-Band Signal Reconstruction: Compressed Sensing for Analog Signals,”
M. Mishali, Y.C. Eldar · 2009
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“A unified framework for the analysis of regularized M M -estimators’,’
S. Negahban, P. Ravikumar, M. J. Wainwright and B. Yu · 2009
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“Convergence of fixed point continuation algorithms for matrix rank minimization,”
D. Goldfarb, S. Ma · 2010
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“Model-based compressive sensing,”
R. Baraniuk, V. Cevher, M. Duarte, and C. Hegde, · 2010
“Compressed Sensing with non-linear observations,”
T. Blumensath · 2010
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W. Xu, M. Wang, A. Tang · 2011
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“Sampling and reconstructing signals from a union of subspaces,”
T. Blumensath, · 2011
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”Combinatorial Selection and Least Absolute Shrinkage via the CLASH Algorithm”
A. Kyrillidis and V. Cevher · 2011
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“Greedy Sparsity-Constrained Optimization,”
Bahmani S., Raj B., Boufounos P · 2012
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Cited alongside, same era.