Constructing Test Instances for Basis Pursuit Denoising

The number of available algorithms for the so-called Basis Pursuit Denoising problem (or the related LASSO-problem) is large and keeps growing. Similarly, the number of experiments to evaluate and compare these algorithms on different instances is growing. In this correspondence, we present a method to produce instances with exact solutions that is based on a simple observation, related to the so-called source condition from sparse regularization.

[1]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[2]  Stephen Becker,et al.  Practical Compressed Sensing: Modern data acquisition and signal processing , 2011 .

[3]  Stephen J. Wright,et al.  Sparse Reconstruction by Separable Approximation , 2008, IEEE Transactions on Signal Processing.

[4]  Dirk A. Lorenz,et al.  Beyond convergence rates: exact recovery with the Tikhonov regularization with sparsity constraints , 2010, 1001.3276.

[5]  Dirk A. Lorenz,et al.  Iterated Hard Shrinkage for Minimization Problems with Sparsity Constraints , 2008, SIAM J. Sci. Comput..

[6]  W. Cheney,et al.  Proximity maps for convex sets , 1959 .

[7]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[8]  Ignace Loris L1Packv2: A Mathematica package for minimizing an l1-penalized functional , 2008, Comput. Phys. Commun..

[9]  Marc Teboulle,et al.  A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..

[10]  Junfeng Yang,et al.  Alternating Direction Algorithms for 1-Problems in Compressive Sensing , 2009, SIAM J. Sci. Comput..

[11]  Joel A. Tropp,et al.  Just relax: convex programming methods for identifying sparse signals in noise , 2006, IEEE Transactions on Information Theory.

[12]  Michael P. Friedlander,et al.  Probing the Pareto Frontier for Basis Pursuit Solutions , 2008, SIAM J. Sci. Comput..

[13]  Wotao Yin,et al.  Bregman Iterative Algorithms for (cid:2) 1 -Minimization with Applications to Compressed Sensing ∗ , 2008 .

[14]  Pablo A. Parrilo,et al.  Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization , 2007, SIAM Rev..

[15]  O. Scherzer,et al.  Necessary and sufficient conditions for linear convergence of ℓ1‐regularization , 2011 .

[16]  Massimo Fornasier,et al.  Recovery Algorithms for Vector-Valued Data with Joint Sparsity Constraints , 2008, SIAM J. Numer. Anal..

[17]  I. Daubechies,et al.  An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.

[18]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[19]  J CandèsEmmanuel,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2011 .

[20]  D. Lorenz,et al.  A semismooth Newton method for Tikhonov functionals with sparsity constraints , 2007, 0709.3186.

[21]  Ronny Ramlau,et al.  REGULARIZATION PROPERTIES OF TIKHONOV REGULARIZATION WITH SPARSITY CONSTRAINTS , 2008 .

[22]  Philip Wolfe,et al.  An algorithm for quadratic programming , 1956 .

[23]  Mário A. T. Figueiredo,et al.  Gradient Projection for Sparse Reconstruction: Application to Compressed Sensing and Other Inverse Problems , 2007, IEEE Journal of Selected Topics in Signal Processing.

[24]  J. Meza,et al.  Steepest descent , 2010 .

[25]  Yin Zhang,et al.  Fixed-Point Continuation for l1-Minimization: Methodology and Convergence , 2008, SIAM J. Optim..

[26]  Marc Teboulle,et al.  A conditional gradient method with linear rate of convergence for solving convex linear systems , 2004, Math. Methods Oper. Res..

[27]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[28]  Emmanuel J. Candès,et al.  Templates for convex cone problems with applications to sparse signal recovery , 2010, Math. Program. Comput..

[29]  O. Scherzer,et al.  Sparse regularization with lq penalty term , 2008, 0806.3222.

[30]  Stephen J. Wright,et al.  Sparse reconstruction by separable approximation , 2009, IEEE Trans. Signal Process..

[31]  Yonina C. Eldar,et al.  Reduce and Boost: Recovering Arbitrary Sets of Jointly Sparse Vectors , 2008, IEEE Transactions on Signal Processing.

[32]  D. Lorenz,et al.  Convergence rates and source conditions for Tikhonov regularization with sparsity constraints , 2008, 0801.1774.

[33]  Michael Elad,et al.  From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images , 2009, SIAM Rev..

[34]  Heinz H. Bauschke,et al.  On Projection Algorithms for Solving Convex Feasibility Problems , 1996, SIAM Rev..

[35]  I. Loris On the performance of algorithms for the minimization of ℓ1-penalized functionals , 2007, 0710.4082.

[36]  Loïc Denis,et al.  Inline hologram reconstruction with sparsity constraints. , 2009, Optics letters.

[37]  Emmanuel J. Candès,et al.  NESTA: A Fast and Accurate First-Order Method for Sparse Recovery , 2009, SIAM J. Imaging Sci..

[38]  Michael P. Friedlander,et al.  Theoretical and Empirical Results for Recovery From Multiple Measurements , 2009, IEEE Transactions on Information Theory.

[39]  Emmanuel J. Candès,et al.  Decoding by linear programming , 2005, IEEE Transactions on Information Theory.