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• W2963459904 abstract "We study verifiable sufficient conditions and computable performance bounds for sparse recovery algorithms such as the Basis Pursuit, the Dantzig selector and the Lasso estimator, in terms of a newly defined family of quality measures for the measurement matrices. With high probability, the developed measures for subgaussian random matrices are bounded away from zero as long as the number of measurements is reasonably large. Comparing to the restricted isotropic constant based performance analysis, the arguments in this paper are much more concise and the obtained bounds are tighter. Numerical experiments are presented to illustrate our theoretical results." @default.
• W2963459904 created "2019-07-30" @default.
• W2963459904 creator A5053535927 @default.
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• W2963459904 date "2019-02-01" @default.
• W2963459904 modified "2023-10-01" @default.
• W2963459904 title "Sparse recovery based on q-ratio constrained minimal singular values" @default.
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• W2963459904 doi "https://doi.org/10.1016/j.sigpro.2018.10.002" @default.
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