Solving polyhedral d.c. optimization problems via concave minimization

Simeon vom Dahl and Andreas Löhne ()
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Simeon vom Dahl: Université Paris-Sud
Andreas Löhne: Friedrich Schiller University Jena

Journal of Global Optimization, 2020, vol. 78, issue 1, No 2, 37-47

Abstract: Abstract The problem of minimizing the difference of two convex functions is called polyhedral d.c. optimization problem if at least one of the two component functions is polyhedral. We characterize the existence of global optimal solutions of polyhedral d.c. optimization problems. This result is used to show that, whenever the existence of an optimal solution can be certified, polyhedral d.c. optimization problems can be solved by certain concave minimization algorithms. No further assumptions are necessary in case of the first component being polyhedral and just some mild assumptions to the first component are required for the case where the second component is polyhedral. In case of both component functions being polyhedral, we obtain a primal and dual existence test and a primal and dual solution procedure. Numerical examples are discussed.

Keywords: Global optimization; D.c. programming; Multi-objective linear programming; Linear vector optimization; 90C26; 90C29; 52B55 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s10898-020-00913-z

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