SPGL1: a solver for large-scale sparse reconstruction problems

Make that six codes (matlab) enabling reconstruction of sparse signals in Compressed Sensing. Ewout van den Berg and Michael Friedlander just made available SPGL1 : A solver for large-scale sparse reconstruction

which can solve both the Basis Pursuit and the Lasso problem.

SPGL1 is a solver for large-scale sparse reconstruction problems. For a given noise-level it can solve

(BP)minimizex1subject toAx–b2

Alternatively, it can solve the underdetermined Lasso problem

(Lasso)minimizeAx–b2subject tox1

for a given . SPGL1 relies only on matrix-vector operations Ax and ATy and accepts both explicit matrices, and functions that evaluate these products. In addition, SPGL1 supports the complex-variables case, and solves the true complex one-norm regularized problem.

The support for complex variables is handy in the case of noiselets.

Resource: Rice Compressed Sensing Library.