@conference {miao2013CSEGaospsa,
	title = {Acceleration on sparse promoting seismic applications},
	booktitle = {CSEG Annual Conference Proceedings},
	year = {2013},
	month = {05},
	abstract = {Sparse promoting oriented problems are never new in seismic applications. Back in 1970s, geophysicists had well exploited the robustness of sparse solutions. Moreover, with the emerging usage of compressed sensing in recent years, sparse recovery have been favored in dealing with {\textquoteright}curse of dimensionality{\textquoteright} in various seismic field acquisition, data processing, and imaging applications. Although sparsity has provided a promising approach, solving for it presents a big challenge. How to work efficiently with the extremely large-scale seismic problem, and how to improve the convergence rate reducing computation time are most frequently asked questions in this content. In this abstract, the author proposed a new algorithm {\textendash} PQN$\ell_1$, trying to address those questions. One example on seismic data processing is included.},
	keywords = {CSEG, Projected Quasi Newton, sparsity-promotion, SPG$\ell_1$},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/CSEG/2013/miao2013CSEGaospsa/miao2013CSEGaospsa.pdf},
	author = {Lina Miao and Felix J. Herrmann}
}
@presentation {miao2012SINBADasp,
	title = {Accelerating on sparse promoting recovery and its benefits in seismic application},
	year = {2012},
	publisher = {SINBAD},
	abstract = {Sparse promoting recovery problem arises more and more frequently with the broad application of compressed sensing tool in exploration seismology. Because of the curse of dimensionality, the prohibitive computation burden on iteratively evaluating objective functions is one of the key issues that constrain high performance l1 solver. In this paper, we try to further improve the convergence performance of SPGl1, one of the state-of-the-art large scale sparse recovery solver, and as a result limit the number of objective function evaluations by introducing a projected quasi Newton method. Examples showing acceleration on seismic data collection, data processing as well as inversion are included.},
	keywords = {Presentation, SINBAD, SINBADFALL2012, SLIM},
	url = {https://slim.gatech.edu/Publications/Public/Conferences/SINBAD/2012/Fall/miao2012SINBADasp/miao2012SINBADasp_pres.pdf},
	author = {Lina Miao}
}
