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Successive Convex Approximation Algorithms for Sparse Signal Estimation With Nonconvex Regularizations
University of Luxembourg.ORCID iD: 0000-0003-2298-6774
2018 (English)In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 12, no 6, p. 1286-1302Article in journal (Refereed) Published
Abstract [en]

In this paper, we propose a successive convex approximation framework for sparse optimization where the nonsmooth regularization function in the objective function is nonconvex and it can be written as the difference of two convex functions. The proposed framework is based on a nontrivial combination of the majorization-minimization framework and the successive convex approximation framework proposed in literature for a convex regularization function. The proposed framework has several attractive features, namely, first, flexibility, as different choices of the approximate function lead to different types of algorithms; second, fast convergence, as the problem structure can be better exploited by a proper choice of the approximate function and the stepsize is calculated by the line search; third, low complexity, as the approximate function is convex and the line search scheme is carried out over a differentiable function; fourth, guaranteed convergence to a stationary point. We demonstrate these features by two example applications in subspace learning, namely the network anomaly detection problem and the sparse subspace clustering problem. Customizing the proposed framework by adopting the best-response type approximation, we obtain soft-thresholding with exact line search algorithms for which all elements of the unknown parameter are updated in parallel according to closed-form expressions. The attractive features of the proposed algorithms are illustrated numerically.

Place, publisher, year, edition, pages
2018. Vol. 12, no 6, p. 1286-1302
Keywords [en]
Convergence, approximation algorithms, upper bound, big data, signal processing algorithms, convex functions, optimization, big data, line search, majorization minimization, nonconvex regularization, successive convex approximation
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:kth:diva-258957DOI: 10.1109/JSTSP.2018.2877584ISI: 000454221700014Scopus ID: 2-s2.0-85053631669OAI: oai:DiVA.org:kth-258957DiVA, id: diva2:1350581
Note

QC 20191030

Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-10-30Bibliographically approved

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