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Sparse Signal Reconstruction: LASSO and Cardinality Approaches Nikita Boyko, Gulver Karamemis, Viktor Kuzmenko, and Stan Uryasev Abstract The paper considers several optimization problem statements for signal sparse reco
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Document Date: 2014-06-24 12:36:20

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Company

IBM / CPLEX LP / Creative Commons / Compressed Sensing Resources / /

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Facility

University of Florida / Viktor Kuzmenko V.M. Glushkov Institute of Cybernetics / /

IndustryTerm

numerical technologies / analytical solution / sparse reconstruction algorithms / sparse solutions / gradient projection algorithm / appropriate numerical tools / imaging / algorithms / /

OperatingSystem

UNIX / /

Organization

Institute of Cybernetics / Gulver Karamemis Department of Information Systems and Operations Management / Nikita Boyko Department of Industrial and Systems Engineering / University of Florida / Stan Uryasev Department of Industrial and Systems Engineering / /

Person

Viktor Kuzmenko / Stan Uryasev / Gulver Karamemis / Nikita Boyko / Viktor Kuzmenko V / /

Position

Correspondent / /

Product

L1 / 64bit / /

ProgrammingLanguage

FL / MATLAB / /

SportsEvent

ufl / /

Technology

geophysics / relevant numerical technologies / specially developed algorithms / UNIX / 2.83 MHz processor / sparse reconstruction algorithms / 2.66 MHz processor / gradient projection algorithm / /

URL

http /

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