Abstract
We study objectives \({\mathcal{F}}_d\) combining a quadratic data-fidelity and an ℓ0 regularization. Data d are generated using a full-rank M ×N matrix A with N > M. Our main results are listed below.
Minimizers of \({\mathcal{F}}_d\) are strict if and only if length(support())\(\leqslant M\) and the submatrix of A whose columns are indexed by support() is full rank. Their continuity in data is derived. Global minimizers are always strict.
We adopt a weak assumption on A and show that it holds with probability one. Data read \(d=A{\ddot{u}}\) where length(support(\({\ddot{u}}\)))\(\leqslant M-1\) and the submatrix whose columns are indexed by support(\({\ddot{u}}\)) is full rank. Among all strict (local) minimizers of \({\mathcal{F}}_d\) with support shorter than M − 1, the exact solution is the unique vector that cancels the residual. The claim is independent of the regularization parameter. This is usually a strict local minimizer where \({\mathcal{F}}_d\) does not reach its global minimum. Global minimization of \({\mathcal{F}}_d\) can then prevent the recovery of \({\ddot{u}}\).
A numerical example (A is 5 ×10) illustrates our main results.
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Nikolova, M. (2012). Should We Search for a Global Minimizer of Least Squares Regularized with an ℓ0 Penalty to Get the Exact Solution of an under Determined Linear System?. In: Bruckstein, A.M., ter Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2011. Lecture Notes in Computer Science, vol 6667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24785-9_43
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DOI: https://doi.org/10.1007/978-3-642-24785-9_43
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