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In this article, we propose an algorithm, NESTA-LASSO, for the LASSO problem, i.e., an underdetermined linear least-squares problem with a 1-norm constraint on the solution.
Rockafellar, R. T.: Convex Analysis. Princeton University Press, Princeton, NJ (1970)
1970
Earlier work this paper cites.
Garey, M. R., Johnson, D.S.: Computers and Intractability. A guide to the theory of NP-completeness. W. H. Freeman, New York, NY (1979)
1979
Earlier work this paper cites.
Dembo, R. S., Eisenstat, S. C., Steihaug, T.: Inexact newton methods. SIAM J. Numer. Anal. 19
1982
Earlier work this paper cites.
Nesterov, Y.: A method for solving the convex programming problem with convergence rate O ( 1 / k 2 ) O(1/k^{2}) . Dokl. Akad. Nauk SSSR 269
1983
Earlier work this paper cites.
Ponomarev, S. P.: Submersions and preimages of sets of measure zero. Siberian Mathematical Journal. 28
1987
Earlier work this paper cites.
Barzilai, J., Borwein, J.: Two point step size gradient method. IMA J. Numer. Anal. 8
1988
Earlier work this paper cites.
Natarajan, B. K.: Sparse approximate solutions to linear systems. SIAM J. Comput. 24
1995
Earlier work this paper cites.
Tibshirani, R.: Regression shrinkage and selection via the lasso
1996
Earlier work this paper cites.
Chen, S., Donoho, D. L., Saunders, M.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20
1998
Earlier work this paper cites.
Bertsekas, D. P.: Nonlinear Programming. Belmont, MA (1999)
1999
Earlier work this paper cites.
Birgin, E., Martínez, J., Raydan, M.: Nonmonotone spectral projected-gradient methods on convex sets. SIAM J. Optim. 10
2000
Earlier work this paper cites.
Osborne, M. R., Presnell, B., Turlach, B. A.: On the lasso
2000
Earlier work this paper cites.
Osborne, M. R., Presnell, B., Turlach, B. A.: A new approach to variable selection in least squares problems. IMA J. Numer. Anal. 20
2000
Earlier work this paper cites.
Efron, B., Hastie, T., Johnstone, I., Tibshirani, R.: Least angle regression. Ann. Statist. 32
2004
Earlier work this paper cites.
Fuchs, J. J.: On sparse representations in arbitrary redundant bases. IEEE Trans. Inf. Th. 1344 (2004)
2004
Cited alongside, same era.
Candès, E. J., Tao, T.: Decoding by linear programming. IEEE Trans. Inform. Theory 51
2005
Cited alongside, same era.
Hennenfent, G., Herrmann, F. J.: Sparseness-constrained data continuation with frames: Applications to missing traces and aliased signals in 2/3-D. SEG Tech. Program Expanded Abstracts 24
2005
Cited alongside, same era.
Nesterov, Y.: Smooth minimization of non-smooth functions. Math. Program. 103
2005
Cited alongside, same era.
Candès, E. J., Romberg, J., Tao, T.: Stable signal recovery from incomplete and inaccurate measurements. Comm. Pure Appl. Math. 59
2006
Cited alongside, same era.
Hale, E. T., Yin, W., Zhang, Y.: Fixed-point continuation for ℓ 1 \ell_{1} -minimization: Methodology and convergence. SIAM J. Optimization 19
2008
Later among the works it cites.
Hennenfent, G., Herrmann, F. J.: Simply denoise: wavefield reconstruction via jittered undersampling. Geophysics 73
2008
Later among the works it cites.
Romberg, J.: Imaging via compressive sensing. IEEE Trans. Signal Process. 25
2008
Later among the works it cites.
Tseng, P.: On accelerated proximal gradient methods for convex-concave optimization. Preprint (2008)
2008
Later among the works it cites.
van den Berg, E., Friedlander, M. P.: Probing the Pareto frontier for basis pursuit solutions. SIAM J. Sci. Comput. 31
2008
Later among the works it cites.
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Donoho, D. L.: For most large underdetermined systems of linear equations the ℓ 1 \ell_{1} -norm solution is also the sparsest solution. Comm. Pure Appl. Math. 59
2006
Cited alongside, same era.
Tropp, J. A.: Just relax: Convex programming methods for identifying sparse signals in noise. IEEE Trans. Inform. Theory 52
2006
Cited alongside, same era.
Candès, E. J., Tao, T.: The Dantzig selector: statistical estimation when p p is much larger than n n . Ann. Statist. 35
2007
Cited alongside, same era.
Figueiredo, M., Nowak, R., Wright, S.: Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems. IEEE J. Selected Top. Signal Process. 1
2007
Cited alongside, same era.
Friedman, J., Hastie, T., Höfling, H., Tibshirani, R.: Pathwise coordinate optimization. Ann. Appl. Stat. 1
2007
Cited alongside, same era.
Hale, E. T., Yin, W., Zhang, Y.: A fixed-point continuation method for ℓ 1 \ell_{1} -regularized minimization with applications to compressed sensing. Rice University Technical Report (2007)
2007
Cited alongside, same era.
Bobin, J., Stark, J.-L., Ottensamer, R.: Compressed sensing in astronomy. IEEE J. Selected Top. Signal Process. 2
2008
Cited alongside, same era.
Yin, W., Osher, S., Goldfarb, D., Darbon, J.: Bregman iterative algorithms for l 1 l_{1} minimization with applications to compressed sensing. SIAM J. Imaging Sci. 1
2008
Later among the works it cites.
Afonso, M., Bioucas-Dias, J., Figueiredo, M.: Fast frame-based image deconvolution using variable splitting and constrained optimization. IEEE/SP 15th Workshop on Statistical Signal Processing, 2009. SSP ’09, 109-112 (2009)
2009
Closest in time.
Beck, A., Teboulle, M.: Fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2
2009
Closest in time.
Figueiredo, M., Nowak, R., Wright, S.: Sparse reconstruction by separable approximation. IEEE Trans. Signal Process. 57
2009
Closest in time.
van den Berg, E., Friedlander, M. P., Hennenfent, G., Herrmann, F. J., Saab, R., Yilmaz, Ö.: Algorithm 890: sparco
2009
Closest in time.
Yu, Y. L.: Nesterov’s optimal gradient method. LLL, Jul. 30, 2009
2009
Closest in time.
Afonso, M., Bioucas-Dias, J., Figueiredo, M.: Fast image recovery using variable splitting and constrained optimization. IEEE Transactions on Image Processing 19
2010
Closest in time.
Wen, Z., Yin, W., Goldfarb, D., Zhang, Y.: A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation. SIAM J. Sci. Comput. 32
2010
Closest in time.
Becker, S., Bobin, J., Candès, E. J.: NESTA: a fast and accurate first-order method for sparse recovery. SIAM J. Imaging Sci. 4
2011
Closest in time.