SparseLab includes a number of papers, with the purpose of allowing the reader to reproduce, adjust, and understand the published results.

David Donoho, High-Dimensional Centrosymmetric Polytopes with Neighborliness Proportional to Dimension.

David Donoho, Neighborly Polytopes and Sparse Solutions of Underdetermined Linear Equations.

David Donoho and Michael Elad, On the Stability of the Basis Pursuit in the Presence of Noise.

David Donoho, Michael Elad, and Vladimir Temlyakov, Stable Recovery of Sparse Overcomplete Representations in the Presence of Noise.

David Donoho and Victoria Stodden, Breakdown Point of Model Selection when the Number of Variables Exceeds the Number of Observations.

David Donoho and Jared Tanner, Sparse Nonnegative Solutions of Underdetermined Linear Equations by Linear Programming.

David Donoho and Jared Tanner, Neighborliness of Randomly Projected Simplices in High Dimensions.

David Donoho and Yaakov Tsaig, Extensions of Compressed Sensing.

David Donoho and Yaakov Tsaig, Fast Solution of l1-norm Minimization Problems When the Solution May be Sparse.

David Donoho, Yaakov Tsaig, Iddo Drori, and Jean-Luc Starck, Sparse Solution of Underdetermined Linear Equations by Stagewise Orthogonal Matching Pursuit.

Michael Elad, Optimized Projections for Compressed-Sensing, IEEE Trans. on Signal Processing, Vol. 55, No. 12, Pages 5695-5702, December 2007.

Michael Elad, Why Simple Shrinkage is Still Relevant for Redundant Representations?

Shihao Ji, David Dunson, and Lawrence Carin, Multi-Task Compressive Sensing.

Shihao Ji, David Dunson, and Lawrence Carin, Bayesian Compressive Sensing.






supported in part by NSF DMS-05-05303.
last updated 20-07-2014 by VCS