An interior proximal linearized method for DC programming based on Bregman distance or second-order homogeneous kernels

We present an interior proximal method for solving constrained nonconvex optimization problems where the objective function is given by the difference of two convex function (DC function). To this end, we consider a linearized proximal method with a proximal distance as regularization. Convergence a...

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Bibliographic Details
Published inOptimization Vol. 68; no. 7; pp. 1305 - 1319
Main Authors Cruz Neto, J. X., Lopes, J. O., Santos, P. S. M., Souza, J. C. O.
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 03.07.2019
Taylor & Francis LLC
Subjects
Bregman distances
DC functions
Interior proximal methods
Linearization
Optimization
proximal distance
Regularization
second-order homogeneous kernels
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Summary:We present an interior proximal method for solving constrained nonconvex optimization problems where the objective function is given by the difference of two convex function (DC function). To this end, we consider a linearized proximal method with a proximal distance as regularization. Convergence analysis of particular choices of the proximal distance as second-order homogeneous proximal distances and Bregman distances are considered. Finally, some academic numerical results are presented for a constrained DC problem and generalized Fermat-Weber location problems.
ISSN:0233-1934
1029-4945
DOI:10.1080/02331934.2018.1476859