On the computational efficiency of subgradient methods: a case study with Lagrangian bounds

  • Subgradient methods (SM) have long been the preferred way to solve the large-scale Nondifferentiable Optimization problems arising from the solution of Lagrangian Duals (LD) of Integer Programs (IP). Although other methods can have better convergence rate in practice, SM have certain advantages that may make them competitive under the right conditions. Furthermore, SMhave significantly progressed in recent years, and new versions have been proposed with better theoretical and practical performances in some applications.We computationally evaluate a large class of SM in order to assess if these improvements carry over to the IP setting. For this we build a unified scheme that covers many of the SMproposed in the literature, comprised some often overlooked features like projection and dynamic generation of variables. We fine-tune the many algorithmic parameters of the resulting large class of SM, and we test them on two different LDs of the Fixed-Charge Multicommodity Capacitated Network Design problem, in order to assess the impact of the characteristics of the problem on the optimal algorithmic choices. Our results show that, if extensive tuning is performed, SM can be competitive with more sophisticated approaches when the tolerance required for solution is not too tight, which is the case when solving LDs of IPs.

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Metadaten
Author:Antonio Frangioni, Bernard Gendron, Enrico Gorgone
DOI:https://doi.org/10.1007/s12532-017-0120-7
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2017
Tag:Software; Theoretical Computer Science
Volume:9
Issue:4
Page Number:32
First Page:573
Last Page:604
Mathematical Programming Computation :MPC 2017 - Issue 4
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