Iterative Thresholding and Related Methods

Hard Thresholding

  1. V. Cevher and S. Jafarpour, Fast Hard Thresholding with Nesterov's Gradient Method.
  2. V. Cevher. On Accelerated Hard Thresholding Methods for Sparse Approximation. Technical report, 2011. [ Details | Full Text ]
  3. V. Cevher. An ALPS view of sparse recovery. Technical report, International Conference on Acoustics, Speech, and Signal Processing, 2011.[ Details | Full Text ]
  4. R. Giryes, V.Cevher, "Online performance guarantees for sparse recovery", to appear in ICASSP 2011.
  5. R Garg, R Khandekar, Gradient Descent with Sparsification: An iterative algorithm for sparse recovery with restricted isometry property Proceedings of the 26th Annual International Conference on Machine Learning, 2009.

Soft Thresholding, Prox Methods, Bregman Iterative Methods

  1. F. Bach, R. Jenatton, J. Mairal and G. Obozinski. Convex optimization with sparsity-inducing norms. In S. Sra, S. Nowozin, S. J. Wright., editors, Optimization for Machine Learning, MIT Press, 2011.
  2. Sundeep Rangan, Approximate Message Passing for Estimation with Random Linear Mixing, ArXiv Oct. 2010.
  3. A. Montanari. Graphical Models Concepts in Compressed Sensing, 2010
  4. M. Bayati and A. Montanari. The dynamics of message passing on dense graphs, with applications to compressed sensing, IEEE Trans. Inform. Theory 2010 (conference version ISIT 2010)
  5. D.L. Donoho, A. Maleki, and A. Montanari. Message passing algorithms for compressed sensing, Proc. Natl Acad. Sci., 2009
  6. D. L. Donoho, A. Maleki, and A. Montanari, "Message passing algorithms for compressed sensing: I. Motivation and construction,"  Proc. of Information Theory Workshop, 2010.
  7. D. L. Donoho, A. Maleki, and A. Montanari, "Message passing algorithms for compressed sensing: II. Analysis and validation,"  Proc. of Information Theory Workshop, 2010.
  8. D. L. Donoho, A. Maleki, and A. Montanari, "Message passing algorithms for compressed sensing," Proc. of National Academy of Sciences (PNAS), November 2009.
  9. A. Maleki, "Convergence analysis of iterative thresholding algorithms,'' Proc. of Allerton Conference on Communication, Control, and Computing, 2009.
  10. Amir Beck and Marc Teboulle, A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems,  SIAM Journal on Imaging Sciences 2 (2009), no. 1, 183--202 MATLAB files.
  11. Amir Beck and Marc Teboulle, Gradient-Based Algorithms with Applications to Signal Recovery Problems,  in "Convex Optimization in Signal Processing and Communications". Editors: Yonina Eldar and Daniel Palomar.  Cambridge university press.
  12. Wotao Yin, Stanley Osher, Donald Goldfarb , and Jerome Darbon.  Bregman Iterative Algorithms for l1-Minimization with Applications to Compressed Sensing. SIAM J. IMAGING SCIENCES, Vol. 1, No. 1, pp. 143–168, 2008.