@presentation {macedo2013SINBADdap,
	title = {A dual approach to PhaseLift via gauge programming and bundle methods},
	year = {2013},
	publisher = {SINBAD},
	abstract = {A feature common to many sparse optimization problems is that the number of variables may be significantly larger than the number of constraints- e.g., the matrix-lifting approach taken by PhaseLift for phase retrieval results in a problem where the number of variables is quadratic in the number of constraints. We consider a duality framework and numerical methods to leverage the relatively small number of constraints. Preliminary numerical results illustrate our approach and its flexibility.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/macedo2013SINBADdap/macedo2013SINBADdap.pdf},
	author = {Ives Macedo and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {pong2013SINBADppg,
	title = {The proximal-proximal gradient algorithm},
	year = {2013},
	publisher = {SINBAD},
	abstract = {In many applications, one has to minimize the sum of a smooth loss function modeling misfit and a regularization term inducing structures. In this talk, we consider the case when the regularization is a composition of a convex function, whose proximal mapping is easy to compute, and a nonzero linear map. Such instances arise in system identification and realization problems. In this talk, we present a new algorithm, the proximal-proximal gradient algorithm, which admits easy subproblems. Our algorithm reduces to the proximal gradient algorithm if the linear map is just the identity map, and can be viewed as "very inexact" inexact proximal gradient algorithm. We show that the whole sequence generated from the algorithm converges to an optimal solution, and establish an upper bound on iteration complexity.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/pong2013SINBADppg/pong2013SINBADppg.pdf},
	author = {Ting Kei Pong and Michael P. Friedlander and Felix J. Herrmann}
}
@presentation {nutini2013SINBADpcb,
	title = {Putting the curvature back into sparse solvers},
	year = {2013},
	publisher = {SINBAD},
	abstract = {For many problems in signal and image processing, we seek a sparse solution that solves that approximately solves the problem Ax $\approx$ b, where A is an m-by-n matrix and b is an m-vector. Many of the most used approaches to problem thissuch as iterative soft thresholding SPGL1 andare first-order methods. As a result, these methods can sometimes be slow to converge. In this talk, we present an approach that takes advantage of the easily-obtainable second-order information. By exploiting this available second-order information, we are able to put the curvature back into sparse solvers and improve upon the convergence rates of existing solvers.},
	keywords = {Presentation, SINBAD, SINBADFALL2013, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2013/Fall/nutini2013SINBADpcb/nutini2013SINBADpcb.pdf},
	author = {Julie Nutini and Michael P. Friedlander and Felix J. Herrmann}
}
@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}
}
