Abstract
A novel methodology is employed to develop algorithms for computing sparse solutions to linear inverse problems, starting from suitably defined diversity measures whose minimization promotes sparsity. These measures include p-norm-like (/spl Lscr//sub (p/spl les/1)/) diversity measures, and the Gaussian and Shannon entropies. The algorithm development methodology uses a factored representation of the gradient, and involves successive relaxation of the Lagrangian necessary condition. The general nature of the methodology provides a systematic approach for deriving a class of algorithms called FOCUSS (FOCal Underdetermined System Solver), and a natural mechanism for extending them.
Keywords
Underdetermined systemInverse problemAlgorithmComputer scienceSparse approximationSolverInverseMinificationMathematical optimizationRelaxation (psychology)GaussianNorm (philosophy)MathematicsApplied mathematics
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Publication Info
- Year
- 2002
- Type
- article
- Volume
- 1
- Pages
- 955-959
- Citations
- 26
- Access
- Closed
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Cite This
Bhaskar D. Rao,
Kenneth Kreutz-Delgado
(2002).
Deriving algorithms for computing sparse solutions to linear inverse problems.
, 1
, 955-959.
https://doi.org/10.1109/acssc.1997.680585
Identifiers
- DOI
- 10.1109/acssc.1997.680585