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• W1609944796 abstract "Significant attention has been given to minimizing a penalized least squares criterion for estimating sparse solutions to large linear systems of equations. The penalty induces sparsity and the natural choice is the so-called l 0 norm. In this paper we develop a Momentumized Iterative Shrinkage Thresholding (MIST) algorithm for minimizing the resulting non-convex criterion and prove its convergence to a local minimizer. Simulations on large data sets show superior performance of the proposed method to other methods." @default.
• W1609944796 created "2016-06-24" @default.
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• W1609944796 date "2015-04-01" @default.
• W1609944796 modified "2023-09-24" @default.
• W1609944796 title "MIST: L<inf>0</inf> sparse linear regression with momentum" @default.
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• W1609944796 doi "https://doi.org/10.1109/icassp.2015.7178632" @default.
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