Hierarchical Convex Optimization by the Hybrid Steepest Descent Method with Proximal Splitting Operators—Enhancements of SVM and Lasso

  • Isao YamadaEmail author
  • Masao Yamagishi


The breakthrough ideas in the modern proximal splitting methodologies allow us to express the set of all minimizers of a superposition of multiple nonsmooth convex functions as the fixed point set of computable nonexpansive operators. In this paper, we present practical algorithmic strategies for the hierarchical convex optimization problems which require further strategic selection of a most desirable vector from the solution set of the standard convex optimization. The proposed algorithms are established by applying the hybrid steepest descent method to special nonexpansive operators designed through the art of proximal splitting. We also present applications of the proposed strategies to certain unexplored hierarchical enhancements of the support vector machine and the Lasso estimator.


Convex optimization Proximal splitting algorithms Hybrid steepest descent method Support Vector Machine (SVM) Lasso TREX Signal processing Machine learning Statistical estimation 

AMS 2010 Subject Classification

49M20 65K10 90C30 



Isao Yamada would like to thank Heinz H. Bauschke, D. Russell Luke, and Regina S. Burachik for their kind encouragement and invitation of the first author to the dream meeting: Splitting Algorithms, Modern Operator Theory, and Applications (September 17–22, 2017) in Oaxaca, Mexico where he had a great opportunity to receive insightful deep comments by Hédy Attouch. He would also like to thank Patrick Louis Combettes and Christian L. Müller for their invitation of the first author to a special mini-symposium Proximal Techniques for High-Dimensional Statistics in the SIAM conference on Optimization 2017 (May 22–25, 2017) in Vancouver. Their kind invitations and their excellent approach to the TREX problem motivated very much the authors to study the application of the proposed strategies to the hierarchical enhancement of Lasso in this paper. Isao Yamada would also like to thank Raymond Honfu Chan for his kind encouragement and invitation to the Workshop on Optimization in Image Processing (June 27–30, 2016) at the Harvard University. Lastly, the authors thank to Yunosuke Nakayama for his help in the numerical experiment related to the proposed hierarchical enhancement of the SVM.


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Authors and Affiliations

  1. 1.Department of Information and Communications EngineeringTokyo Institute of TechnologyTokyoJapan

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