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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.

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1

Solving systems of monotone inclusions via primal-dual splitting techniques

Bot, Radu Ioan, Csetnek, Ernö Robert, Nagy, Erika 20 March 2013 (has links) (PDF)
In this paper we propose an algorithm for solving systems of coupled monotone inclusions in Hilbert spaces. The operators arising in each of the inclusions of the system are processed in each iteration separately, namely, the single-valued are evaluated explicitly (forward steps), while the set-valued ones via their resolvents (backward steps). In addition, most of the steps in the iterative scheme can be executed simultaneously, this making the method applicable to a variety of convex minimization problems. The numerical performances of the proposed splitting algorithm are emphasized through applications in average consensus on colored networks and image classification via support vector machines.
convexe optimierung
algorithmen
convex minimization
coupled systems
forward-backward-forward algorithm
monotone inclusion
operator splitting
47H05
65K05
90C25
90C46
ddc:510
Algorithmus
Operator-Splitting-Verfahren
2

Solving systems of monotone inclusions via primal-dual splitting techniques

Bot, Radu Ioan, Csetnek, Ernö Robert, Nagy, Erika 20 March 2013 (has links)
In this paper we propose an algorithm for solving systems of coupled monotone inclusions in Hilbert spaces. The operators arising in each of the inclusions of the system are processed in each iteration separately, namely, the single-valued are evaluated explicitly (forward steps), while the set-valued ones via their resolvents (backward steps). In addition, most of the steps in the iterative scheme can be executed simultaneously, this making the method applicable to a variety of convex minimization problems. The numerical performances of the proposed splitting algorithm are emphasized through applications in average consensus on colored networks and image classification via support vector machines.
info:eu-repo/classification/ddc/510
ddc:510
convexe optimierung, algorithmen
47H05, 65K05, 90C25, 90C46

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