Mathematics – Numerical Analysis
Scientific paper
2011-04-06
Inverse Problems 27 (2011) 125007
Mathematics
Numerical Analysis
17 pages; 1 figure; results formulated for a more general penalty than previous version; numerical example added
Scientific paper
10.1088/0266-5611/27/12/125007
An explicit algorithm for the minimization of an $\ell_1$ penalized least squares functional, with non-separable $\ell_1$ term, is proposed. Each step in the iterative algorithm requires four matrix vector multiplications and a single simple projection on a convex set (or equivalently thresholding). Convergence is proven and a 1/N convergence rate is derived for the functional. In the special case where the matrix in the $\ell_1$ term is the identity (or orthogonal), the algorithm reduces to the traditional iterative soft-thresholding algorithm. In the special case where the matrix in the quadratic term is the identity (or orthogonal), the algorithm reduces to a gradient projection algorithm for the dual problem. By replacing the projection with a simple proximity operator, other convex non-separable penalties than those based on an $\ell_1$-norm can be handled as well.
Loris Ignace
Verhoeven Caroline
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