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Lagrangian bounds for large‐scale multicommodity network design: a comparison between Volume and Bundle methods
Author(s) -
Shibasaki Rui S.,
Baiou Mourad,
Barahona Francisco,
Mahey Philippe,
Souza Mauricio C.
Publication year - 2021
Publication title -
international transactions in operational research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.032
H-Index - 52
eISSN - 1475-3995
pISSN - 0969-6016
DOI - 10.1111/itor.12770
Subject(s) - bundle , mathematical optimization , lagrangian relaxation , mathematics , scale (ratio) , dual (grammatical number) , heuristic , lagrangian , computer science , art , materials science , physics , literature , quantum mechanics , composite material
The Bundle Method and the Volume Algorithm are among the most efficient techniques to obtain accurate Lagrangian dual bounds for hard combinatorial optimization problems. We propose here to compare their performance on very large scale Fixed‐Charge Multicommodity Capacitated Network Design problems. The motivation is not only the quality of the approximation of these bounds as a function of the computational time but also the ability to produce feasible primal solutions and thus to reduce the gap for very large instances for which optimal solutions are out of reach. Feasible solutions are obtained through the use of Lagrangian information in constructive and improving heuristic schemes. We show in particular that, if the Bundle implementation has provided great quality bounds in fewer iterations, the Volume Algorithm is able to reduce the gaps of the largest instances, taking profit of the low computational cost per iteration compared to the Bundle Method.

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