HAL : hal-00723812, version 6
 arXiv : 1304.5218
 Versions disponibles : v1 (14-08-2012) v2 (01-10-2012) v3 (28-11-2012) v4 (16-01-2013) v5 (18-04-2013) v6 (15-05-2013)
 Description of the minimizers of least squares regularized~ with~ $\bm{\ell_0}$-norm. Uniqueness of the global minimizer
 (11/05/2013)
 We have an $\m\x\n$ real-valued arbitrary matrix $A$ (e.g. a dictionary) with $\m<\n$ and data $d$ describing the sought-after object with the help of $A$. This work provides an in-depth analysis of the (local and global) minimizers of an objective function $\Fd$ combining a quadratic data-fidelity term and an $\ell_0$ penalty applied to each entry of the sought-after solution, weighted by a regularization parameter $\be>0$. For several decades, this objective has attracted a ceaseless effort to conceive algorithms approaching a good minimizer. Our theoretical contributions, summarized below, shed new light on the existing algorithms and can help the conception of innovative numerical schemes. To solve the normal equation associated with any $\m$-row submatrix of $A$ is equivalent to compute a local minimizer $\hu$ of $\Fd$. (Local) minimizers $\hu$ of $\Fd$ are strict if and only if the submatrix, composed of those columns of $A$ whose indexes form the support of $\hu$, has full column rank. An outcome is that strict local minimizers of $\Fd$ are easily computed without knowing the value of $\be$. Each strict local minimizer is linear in data. It is proved that $\Fd$ has global minimizers and that they are always strict. They are studied in more details under the (standard) assumption that $\rank(A)=\m<\n$. The global minimizers with $\m$-length support are seen to be impractical. Given $d$, critical values $\be_\k$ for any $\k\leq\m-1$ are exhibited such that if $\be>\be_\k$, all global minimizers of $\Fd$ are $\k$-sparse. An assumption on $A$ is adopted and proved to fail only on a closed negligible subset. Then for all data $d$ beyond a closed negligible subset, the objective $\Fd$ for $\be>\be_\k$, $\k\leq\m-1$, has a unique global minimizer and this minimizer is $\k$-sparse. Instructive small-size ($5\x 10$) numerical illustrations confirm the main theoretical results.
 1 : Centre de Mathématiques et de Leurs Applications (CMLA) CNRS : UMR8536 – École normale supérieure de Cachan - ENS Cachan
 Domaine : Mathématiques/Analyse numérique
 Mots Clés : asymptotically level stable functions – global minimizers – local minimizers – $\ell_0$ regularization – nonconvex nonsmooth minimization – perturbation analysis – quadratic programming – solution analysis – sparse recovery – strict minimizers – underdetermined linear systems – uniqueness of the solution – variational methods
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 hal-00723812, version 6 http://hal.archives-ouvertes.fr/hal-00723812 oai:hal.archives-ouvertes.fr:hal-00723812 Contributeur : Mila Nikolova <> Soumis le : Mardi 14 Mai 2013, 17:45:37 Dernière modification le : Mercredi 15 Mai 2013, 16:12:11