 Selective linearization for multi-block statistical learningYu Du, Xiaodong Lin, Minh Pham and Andrzej Ruszczyński European Journal of Operational Research, 2021, vol. 293, issue 1, 219-228 Abstract: We consider the problem of minimizing a sum of several convex non-smooth functions and discuss the selective linearization method (SLIN), which iteratively linearizes all but one of the functions and employs simple proximal steps. The algorithm is a form of multiple operator splitting in which the order of processing partial functions is not fixed, but rather determined in the course of calculations. SLIN is globally convergent for an arbitrary number of component functions without artificial duplication of variables. We report results from extensive numerical experiments in two statistical learning settings such as large-scale overlapping group Lasso and doubly regularized support vector machine. In each setting, we introduce novel and efficient solutions for solving sub-problems. The numerical results demonstrate the efficacy and accuracy of SLIN. Keywords: Nonlinear programming; Statistical learning; Penalized regression; Regularized support vector machine (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:293:y:2021:i:1:p:219-228 DOI: 10.1016/j.ejor.2020.12.010 Access Statistics for this article European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati More articles in European Journal of Operational Research from Elsevier |
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