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• W2010333852 abstract "In this paper, we develop verifiable and computable performance analysis of the l ∞ norms of the recovery errors for l 1 minimization algorithms. We define a family of goodness measures for arbitrary sensing matrices as a set of optimization problems, and design algorithms with a theoretical global convergence guarantee to compute these goodness measures. The proposed algorithms solve a series of second-order cone programs, or linear programs. As a by-product, we implement an efficient algorithm to verify a sufficient condition for exact l 1 recovery in the noise-free case. This implementation performs orders-of-magnitude faster than the state-of-the-art techniques. We derive performance bounds on the l ∞ norms of the recovery errors in terms of these goodness measures. We establish connections between other performance criteria (e.g., the l 2 norm, l 1 norm, and support recovery) and the l ∞ norm in a tight manner. We also analytically demonstrate that the developed goodness measures are non-degenerate for a large class of random sensing matrices, as long as the number of measurements is relatively large. Numerical experiments show that, compared with the restricted isometry based performance bounds, our error bounds apply to a wider range of problems and are tighter, when the sparsity levels of the signals are relatively low." @default.
• W2010333852 created "2016-06-24" @default.
• W2010333852 creator A5031212309 @default.
• W2010333852 creator A5065829773 @default.
• W2010333852 date "2011-03-01" @default.
• W2010333852 modified "2023-10-15" @default.
• W2010333852 title "Verifiable and computable &#x2113;<inf>&#x221E;</inf> performance evaluation of &#x2113;<inf>1</inf> sparse signal recovery" @default.
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• W2010333852 doi "https://doi.org/10.1109/ciss.2011.5766115" @default.
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