@article {vandenberg2011SIAMsol,
	title = {Sparse optimization with least-squares constraints},
	journal = {SIAM Journal on Optimization},
	volume = {21},
	number = {4},
	year = {2011},
	month = {11},
	pages = {1201{\textendash}1229},
	abstract = {The use of convex optimization for the recovery of sparse signals from incomplete or compressed data is now common practice. Motivated by the success of basis pursuit in recovering sparse vectors, new formulations have been proposed that take advantage of different types of sparsity. In this paper we propose an efficient algorithm for solving a general class of sparsifying formulations. For several common types of sparsity we provide applications, along with details on how to apply the algorithm, and experimental results.},
	keywords = {basis pursuit, compressed sensing, convex program, duality, group sparsity, matrix completion, Newton{\textquoteright}s method, root-finding, sparse solutions},
	doi = {10.1137/100785028},
	url = {http://www.math.ucdavis.edu/\%7Empf/2010-sparse-optimization-with-least-squares.html},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
@article {vandenberg2009ACMstf,
	title = {Sparco: a testing framework for sparse reconstruction},
	journal = {ACM Transactions on Mathematical Software},
	volume = {35},
	number = {4},
	year = {2009},
	month = {02},
	pages = {1-16},
	abstract = {Sparco is a framework for testing and benchmarking algorithms for sparse reconstruction. It includes a large collection of sparse reconstruction problems drawn from the imaging, compressed sensing, and geophysics literature. Sparco is also a framework for implementing new test problems and can be used as a tool for reproducible research. Sparco is implemented entirely in Matlab, and is released as open-source software under the GNU Public License.},
	keywords = {compressed sensing, linear operators, Sparse recovery},
	url = {http://doi.acm.org/10.1145/1462173.1462178},
	author = {Ewout van den Berg and Michael P. Friedlander and Gilles Hennenfent and Felix J. Herrmann and Rayan Saab and Ozgur Yilmaz}
}
@conference {friedlander2009SCAIMspot,
	title = {Spot: A linear-operator toolbox for Matlab},
	booktitle = {SCAIM},
	year = {2009},
	publisher = {SCAIM Seminar},
	organization = {SCAIM Seminar},
	address = {University of British Columbia},
	keywords = {minimization, Presentation, SLIM},
	presentation = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2009/VandenBerg-Mon-1130.pdf},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
@conference {vandenberg2008SINBADsat,
	title = {Sparco: A testing framework for sparse reconstruction},
	booktitle = {SINBAD 2008},
	year = {2008},
	abstract = {Sparco is a framework for testing and benchmarking algorithms for sparse reconstruction. It includes a large collection of sparse reconstruction problems drawn from the imaging, compressed sensing, and geophysics literature. Sparco is also a framework for implementing new test problems and can be used as a tool for reproducible research. We describe the software environment, and demonstrate its usefulness for testing and comparing solvers for sparse reconstruction.},
	keywords = {Presentation, SINBAD, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2008/vandenberg2008SINBADsat/vandenberg2008SINBADsat.pdf},
	author = {Ewout van den Berg}
}
@manual {vandenberg07MNsat,
	title = {SPARCO: a toolbox for testing sparse reconstruction algorithms},
	year = {2007},
	month = {10},
	abstract = {Sparco is a suite of problems for testing and benchmarking algorithms for sparse signal reconstruction. It is also an environment for creating new test problems, and a suite of standard linear operators is provided from which new problems can be assembled. Sparco is implement ed entirely in Matlab and is self contained. (A few optional test problems are based on the CurveLab toolbox, which can be installed separately.) At the core of the sparse recovery problem is the linear system $Ax+r=b$, where $A$ is an $m$-by-$n$ linear operator and the $m$-vector $b$ is the observed signal. The goal is to find a sparse $n$-vector $x$ such that $r$ is small in norm.},
	keywords = {SLIM},
	url = {http://www.cs.ubc.ca/labs/scl/sparco/},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
