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Volumn 1, Issue , 2012, Pages 839-846

A proximal-gradient homotopy method for the ℓ 1-regularized least-squares problem

Author keywords

[No Author keywords available]

Indexed keywords

COMPRESSIVE SENSING; CONVERGENCE RATES; HOMOTOPY CONTINUATION; HOMOTOPY METHOD; HOMOTOPY SOLUTION PATHS; LEAST SQUARE; OBJECTIVE FUNCTIONS; REGULARIZATION PARAMETERS; SOLUTION PATH; SPARSE RECOVERY; SUBLINEAR;

EID: 84867128532     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (22)

References (20)
  • 1
    • 79551550744 scopus 로고    scopus 로고
    • NESTA: A fast and accurate first-order method for sparse recovery
    • Becker, S. R., Bobin, J., and Candès, E. J. NESTA: A fast and accurate first-order method for sparse recovery. SIAM Journal on Imaging Sciences, 4(1):1-39, 2011.
    • (2011) SIAM Journal on Imaging Sciences , vol.4 , Issue.1 , pp. 1-39
    • Becker, S.R.1    Bobin, J.2    Candès, E.J.3
  • 2
    • 68649086910 scopus 로고    scopus 로고
    • Simultaneous analysis of Lasso and Dantzig selector
    • Bickel, P., Ritov, Y., and Tsybakov, A. Simultaneous analysis of Lasso and Dantzig selector. Annals of Statistics, 37:1705-1732, 2009.
    • (2009) Annals of Statistics , vol.37 , pp. 1705-1732
    • Bickel, P.1    Ritov, Y.2    Tsybakov, A.3
  • 3
    • 36749005582 scopus 로고    scopus 로고
    • A new TwIST: Two-step iterative shrinking/thresholding algorithms for image restoration
    • Bioucas-Dias, J. M. and Figueiredo, M. A. T. A new TwIST: Two-step iterative shrinking/thresholding algorithms for image restoration. IEEE Transactions on Image Processing, 16(12):2992-3004, 2007.
    • (2007) IEEE Transactions on Image Processing , vol.16 , Issue.12 , pp. 2992-3004
    • Bioucas-Dias, J.M.1    Figueiredo, M.A.T.2
  • 5
    • 7044231546 scopus 로고    scopus 로고
    • An iterative thresholding algorithm for linear inverse problems with a sparsity constraint
    • Daubechies, I., Defriese, M., and Mol, C. De. An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Comm. Pure Appl. Math., 57(11):1413-1457, 2004.
    • (2004) Comm. Pure Appl. Math. , vol.57 , Issue.11 , pp. 1413-1457
    • Daubechies, I.1    Defriese, M.2    De Mol, C.3
  • 8
    • 72249100613 scopus 로고    scopus 로고
    • The dantzig selector and sparsity oracle inequalities
    • Koltchinskii, V. The dantzig selector and sparsity oracle inequalities. Bernoulli, 15:799-828, 2009.
    • (2009) Bernoulli , vol.15 , pp. 799-828
    • Koltchinskii, V.1
  • 9
    • 33747163541 scopus 로고    scopus 로고
    • High dimensional graphs and variable selection with the lasso
    • Meinshausen, N. and Bühlmann, P. High dimensional graphs and variable selection with the lasso. Annals of Statistics, 34:1436-1462, 2006.
    • (2006) Annals of Statistics , vol.34 , pp. 1436-1462
    • Meinshausen, N.1    Bühlmann, P.2
  • 11
    • 17444406259 scopus 로고    scopus 로고
    • Smooth minimization of nonsmooth functions
    • Nesterov, Yu. Smooth minimization of nonsmooth functions. Mathematical Programming, 103(1):127-152, 2005.
    • (2005) Mathematical Programming , vol.103 , Issue.1 , pp. 127-152
    • Nesterov, Yu.1
  • 12
    • 57649169327 scopus 로고    scopus 로고
    • Gradient methods for minimizing composite objective function
    • Center for Operations Research and Econometrics, Catholic University of Louvain, Belgium, September
    • Nesterov, Yu. Gradient methods for minimizing composite objective function. CORE discussion paper 2007/76, Center for Operations Research and Econometrics, Catholic University of Louvain, Belgium, September 2007.
    • (2007) CORE Discussion Paper 2007/76
    • Nesterov, Yu.1
  • 15
    • 70049111607 scopus 로고    scopus 로고
    • On accelerated proximal gradient methods for convex-concave optimization
    • Manuscript submitted to
    • Tseng, P. On accelerated proximal gradient methods for convex-concave optimization. Manuscript submitted to SIAM Journal on Optimization, 2008.
    • (2008) SIAM Journal on Optimization
    • Tseng, P.1
  • 16
    • 77955054299 scopus 로고    scopus 로고
    • On the conditions used to prove oracle results for the lasso
    • van de Geer, S. and Bühlmann, P. On the conditions used to prove oracle results for the lasso. Electronic Journal of Statistics, 3:1360-1392, 2009.
    • (2009) Electronic Journal of Statistics , vol.3 , pp. 1360-1392
    • Van De Geer, S.1    Bühlmann, P.2
  • 17
    • 77955666600 scopus 로고    scopus 로고
    • A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation
    • Wen, Z., Yin, W., Goldfarb, D., and Zhang, Y. A fast algorithm for sparse reconstruction based on shrinkage, subspace optimization and continuation. SIAM Journal on Scientific Computing, 32(4):1832-1857, 2010.
    • (2010) SIAM Journal on Scientific Computing , vol.32 , Issue.4 , pp. 1832-1857
    • Wen, Z.1    Yin, W.2    Goldfarb, D.3    Zhang, Y.4
  • 19
    • 50949096321 scopus 로고    scopus 로고
    • The sparsity and bias of the lasso selection in high-dimensional linear regression
    • Zhang, C.-H. and Huang, J. The sparsity and bias of the lasso selection in high-dimensional linear regression. Annals of Statistics, 36:1567-1594, 2008.
    • (2008) Annals of Statistics , vol.36 , pp. 1567-1594
    • Zhang, C.-H.1    Huang, J.2
  • 20
    • 69049086702 scopus 로고    scopus 로고
    • 1 regularization
    • 1 regularization. Annals of Statistics, 37:2109-2144, 2009.
    • (2009) Annals of Statistics , vol.37 , pp. 2109-2144
    • Zhang, T.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.