Solving Regularized Multifacility Location Problems with Unknown Number of Centers via Difference-of-Convex Optimization

Fuente: arXiv
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Autores principales: Geremew, W., Long, V. S. T., Nam, N. M., Solano-Herrera, A.
Formato: Preprint
Publicado: 2026
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author Geremew, W.
Long, V. S. T.
Nam, N. M.
Solano-Herrera, A.
author_facet Geremew, W.
Long, V. S. T.
Nam, N. M.
Solano-Herrera, A.
contents In this paper, we develop optimization methods for a new model of multifacility location problems defined by a Minkowski gauge with Laplace-type regularization terms. The model is analyzed from both theoretical and numerical perspectives. In particular, we establish the existence of optimal solutions and study qualitative properties of global minimizers. By combining Nesterov's smoothing technique with recent advances in difference-of-convex optimization, following the pioneering work of P. D. Tao and L. T. H. An and others, we propose efficient numerical algorithms for minimizing the objective function of this model. As an application, our approach provides an effective method for determining the number of centers in gauge-based multifacility location and clustering problems. Our results extend and complement recent developments.
format Preprint
id arxiv_https___arxiv_org_abs_2601_16576
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Solving Regularized Multifacility Location Problems with Unknown Number of Centers via Difference-of-Convex Optimization
Geremew, W.
Long, V. S. T.
Nam, N. M.
Solano-Herrera, A.
Optimization and Control
49J52, 68Q25, 90C26, 90C90, 90C31, 90C35
In this paper, we develop optimization methods for a new model of multifacility location problems defined by a Minkowski gauge with Laplace-type regularization terms. The model is analyzed from both theoretical and numerical perspectives. In particular, we establish the existence of optimal solutions and study qualitative properties of global minimizers. By combining Nesterov's smoothing technique with recent advances in difference-of-convex optimization, following the pioneering work of P. D. Tao and L. T. H. An and others, we propose efficient numerical algorithms for minimizing the objective function of this model. As an application, our approach provides an effective method for determining the number of centers in gauge-based multifacility location and clustering problems. Our results extend and complement recent developments.
title Solving Regularized Multifacility Location Problems with Unknown Number of Centers via Difference-of-Convex Optimization
topic Optimization and Control
49J52, 68Q25, 90C26, 90C90, 90C31, 90C35
url https://arxiv.org/abs/2601.16576