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

Extensions of Compressed Sensing, David Donoho and Yaakov Tsaig.

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 Jared Tanner.

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

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

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

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

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

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

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

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

Bayesian Compressive Sensing, Shihao Ji, Ya Xue, and Lawrence Carin.

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






supported in part by NSF DMS-05-05303.
last updated 27-12-2005 by VCS