Mixed integer nonlinear optimization models for the Euclidean Steiner tree problem in $$\mathbb {R}^d$$ R d

Hacene Ouzia () and Nelson Maculan ()
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Hacene Ouzia: Sorbonne Universite, CNRS, LIP-6
Nelson Maculan: Universidade Federal do Rio de Janeiro, COPPE & IM

Journal of Global Optimization, 2022, vol. 83, issue 1, No 7, 119-136

Abstract: Abstract New mixed integer nonlinear optimization models for the Euclidean Steiner tree problem in d-space (with $$d\ge 3$$ d ≥ 3 ) will be presented in this work. All models feature a nonsmooth objective function but the continuous relaxations of their set of feasible solutions are convex. From these models, four convex mixed integer linear and nonlinear relaxations will be considered. Each relaxation has the same set of feasible solutions as the set of feasible solutions of the model from which it is derived. Finally, preliminary computational results highlighting the main features of the presented relaxations will be discussed.

Keywords: Integer Programming; Euclidean Steiner tree problem; Steiner tree; Nonlinear optimization models; Mixed integer nonlinear optimization; Relaxation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10898-021-01001-6

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