Journal article
Implementation of an optimal first-order method for strongly convex total variation regularization
We present a practical implementation of an optimal first-order method, due to Nesterov, for large-scale total variation regularization in tomographic reconstruction, image deblurring, etc. The algorithm applies to μ-strongly convex objective functions with L-Lipschitz continuous gradient. In the framework of Nesterov both μ and L are assumed known—an assumption that is seldom satisfied in practice.
We propose to incorporate mechanisms to estimate locally sufficient μ and L during the iterations. The mechanisms also allow for the application to non-strongly convex functions. We discuss the convergence rate and iteration complexity of several first-order methods, including the proposed algorithm, and we use a 3D tomography problem to compare the performance of these methods.
In numerical simulations we demonstrate the advantage in terms of faster convergence when estimating the strong convexity parameter μ for solving ill-conditioned problems to high accuracy, in comparison with an optimal method for non-strongly convex problems and a first-order method with Barzilai-Borwein step size selection.
Language: | English |
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Publisher: | Springer Netherlands |
Year: | 2012 |
Pages: | 329-356 |
ISSN: | 15729125 and 00063835 |
Types: | Journal article |
DOI: | 10.1007/s10543-011-0359-8 |
ORCIDs: | Jørgensen, Jakob Heide and Hansen, Per Christian |