@presentation {goh2013SINBADttt,
	title = {Taming time through tangents},
	year = {2013},
	publisher = {SINBAD},
	abstract = {Given two vectors of (possibly) different lengths, the edit distance considers all possible alignments between the two and picks the one that minimizes the number of operations needed to turn one into the other. Though highly non-smooth and riddled with local minima, we show a way to compute the convex envelope of this function, which opens the door to using the approximate edit distance as a surrogate for the L2 distance and comparing vectors of different lengths.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/goh2013SINBADttt/goh2013SINBADttt.pdf},
	author = {Gabriel Goh and Michael P. Friedlander and Felix J. Herrmann}
}
@article {vandenberg2010IEEEter,
	title = {Theoretical and empirical results for recovery from multiple measurements},
	journal = {IEEE Transactions on Information Theory},
	volume = {56},
	number = {5},
	year = {2010},
	month = {05},
	pages = {2516-2527},
	abstract = {The joint-sparse recovery problem aims to recover, from sets of compressed measurements, unknown sparse matrices with nonzero entries restricted to a subset of rows. This is an extension of the single-measurement-vector (SMV) problem widely studied in compressed sensing. We analyze the recovery properties for two types of recovery algorithms. First, we show that recovery using sum-of-norm minimization cannot exceed the uniform recovery rate of sequential SMV using L1 minimization, and that there are problems that can be solved with one approach but not with the other. Second, we analyze the performance of the ReMBo algorithm [M. Mishali and Y. Eldar, IEEE Trans. Sig. Proc., 56 (2008)] in combination with L1 minimization, and show how recovery improves as more measurements are taken. From this analysis it follows that having more measurements than number of nonzero rows does not improve the potential theoretical recovery rate.},
	keywords = {convex optimization, joint sparsity, multiple channels, Sparse recovery},
	doi = {10.1109/TIT.2010.2043876},
	url = {http://www.math.ucdavis.edu/\%7Empf/2010-joint-sparsity.html},
	author = {Ewout van den Berg and Michael P. Friedlander}
}
