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• W2953014734 abstract "The convergence rate is analyzed for the SpaSRA algorithm (Sparse Reconstruction by Separable Approximation) for minimizing a sum $f (m{x}) + psi (m{x})$ where $f$ is smooth and $psi$ is convex, but possibly nonsmooth. It is shown that if $f$ is convex, then the error in the objective function at iteration $k$, for $k$ sufficiently large, is bounded by $a/(b+k)$ for suitable choices of $a$ and $b$. Moreover, if the objective function is strongly convex, then the convergence is $R$-linear. An improved version of the algorithm based on a cycle version of the BB iteration and an adaptive line search is given. The performance of the algorithm is investigated using applications in the areas of signal processing and image reconstruction." @default.
• W2953014734 created "2019-06-27" @default.
• W2953014734 creator A5002362913 @default.
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• W2953014734 date "2009-12-09" @default.
• W2953014734 modified "2023-10-01" @default.
• W2953014734 title "Gradient-based methods for sparse recovery" @default.
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• W2953014734 hasPublicationYear "2009" @default.
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