A finite hyperplane traversal Algorithm for 1-dimensional $$L^1pTV$$ L 1 p T V minimization, for $$0>p\le 1$$ 0 > p ≤ 1

Heather Moon () and Thomas Asaki ()

Computational Optimization and Applications, 2015, vol. 61, issue 3, 783-818

Abstract: In this paper, we consider a discrete formulation of the one-dimensional $$L^1pTV$$ L 1 p T V functional and introduce a finite algorithm that finds exact minimizers of this functional for $$0>p\le 1$$ 0 > p ≤ 1 . Our algorithm for the special case for $$L^1TV$$ L 1 T V returns globally optimal solutions for all regularization parameters $$\lambda \ge 0$$ λ ≥ 0 at the same computational cost of determining a single optimal solution associated with a particular value of $$\lambda $$ λ . This finite set of minimizers contains the scale signature of the known initial data. A variation on this algorithm returns locally optimal solutions for all $$\lambda \ge 0$$ λ ≥ 0 for the case when $$0>p>1$$ 0 > p > 1 . The algorithm utilizes the geometric structure of the set of hyperplanes defined by the nonsmooth points of the $$L^1pTV$$ L 1 p T V functional. We discuss efficient implementations of the algorithm for both general and binary data. Copyright Springer Science+Business Media New York 2015

Keywords: Signal processing; Nonsmooth optimization; Scales (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/s10589-015-9738-4

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