A matheuristic approach for the maximum balanced subgraph of a signed graph

Open Access

Issue		RAIRO-Oper. Res. Volume 55, Number 5, September-October 2021


Page(s)		3121 - 3140
DOI		https://doi.org/10.1051/ro/2021150
Published online		21 October 2021

R.M. Aiex, M.G.C. Resende and C.C. Ribeiro, Ttt plots: a perl program to create time-to-target plots. Optim. Lett. 1 (2007) 355–366. [CrossRef] [MathSciNet] [Google Scholar]
F. Barahona and A.R. Mahjoub, Facets of the balanced (acyclic) induced subgraph polytope. Math. Program. 45 (1989) 21–33. [CrossRef] [Google Scholar]
M. Birattari, Z. Yuan, P. Balaprakash and T. Stützle, F-race and iterated f-race: An overview. In: Experimental methods for the analysis of optimization algorithms. Springer (2010) 311–336. [CrossRef] [Google Scholar]
D. Cartwright and F. Harary, Structural balance: a generalization of heider’s theory. Psychol. Rev. 63 (1956) 277. [CrossRef] [PubMed] [Google Scholar]
B. DasGupta, G. Enciso, E. Sontag and Y. Zhang, Algorithmic and complexity results for decompositions of biological networks into monotone subsystems. Biosystems 90 (2006) 161–178. [Google Scholar]
M.R. de Holanda Maia, A. Plastino and P.H.V. Penna, Minereduce: an approach based on data mining for problem size reduction. Comput. Oper. Res. 122 (2020) 104995. [CrossRef] [MathSciNet] [Google Scholar]
N. Dupin and E.-G. Talbi, Parallel matheuristics for the discrete unit commitment problem with minstop ramping constraints. Int. Trans. Oper. Res. 27 (2020) 219–244. [CrossRef] [MathSciNet] [Google Scholar]
R. Figueiredo and Y. Frota, The maximum balanced subgraph of a signed graph: Applications and solution approaches. Eur. J. Oper. Res. 236 (2014) 473–487. [CrossRef] [Google Scholar]
R.M.V. Figueiredo, M. Labbé and C.C. De Souza, An exact approach to the problem of extracting an embedded network matrix. Comput. Oper. Res. 38 (2011) 1483–1492. [CrossRef] [MathSciNet] [Google Scholar]
M. Fischetti and M. Fischetti, Matheuristics. In: Handbook of Heuristics. Springer (2018) 121–153. [CrossRef] [Google Scholar]
N. Gülpinar, G. Gutin, G. Mitra and A. Zverovitch, Extracting pure network submatrices in linear programs using signed graphs. Discrete Appl. Math. 137 (2004) 359–372. [CrossRef] [MathSciNet] [Google Scholar]
F. Harary, M.-H. Lim and D.C. Wunsch, Signed graphs for portfolio analysis in risk management. IMA J. Manage. Math. 13 (2002) 201–210. [CrossRef] [Google Scholar]
F. Heider, Attitudes and cognitive organization. J. Psychology 21 (1946) 107–112. [Google Scholar]
F. Hüffner, N. Betzler and R. Niedermeier, Separator-based data reduction for signed graph balancing. J. Comb. Optim. 20 (2010) 335–360. [CrossRef] [MathSciNet] [Google Scholar]
M. López-Ibáñez, J. Dubois-Lacoste, L.P. Cáceres, M. Birattari and T. Stützle, The irace package: Iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3 (2016) 43–58. [MathSciNet] [Google Scholar]
F. Marinelli and A. Parente, A heuristic based on negative chordless cycles for the maximum balanced induced subgraph problem. Comput. Oper. Res. 69 (2016) 68–78. [CrossRef] [MathSciNet] [Google Scholar]
B. Martn, Á. Sánchez, C. Beltran-Royo and A. Duarte, A matheuristic approach for solving the edge-disjoint paths problem. Matheuristics 2016 (2016) 25. [Google Scholar]
D. Martins, G.M. Vianna, I. Rosseti, S.L. Martins and A. Plastino, Making a state-of-the-art heuristic faster with data mining. Ann. Oper. Res. 263 (2018) 141–162. [CrossRef] [MathSciNet] [Google Scholar]
M.G.C. Resende and C.C. Ribeiro, Greedy randomized adaptive search procedures: advances and extensions. In: Handbook of metaheuristics. Springer (2019) 169–220. [CrossRef] [Google Scholar]
S. Wolfram, Wolfram Research. Inc., Mathematica, Version 8 (2013) 23. [Google Scholar]
T. Zaslavsky, A mathematical bibliography of signed and gain graphs and allied areas. Electron. J. Comb. 1000 (2012) DS8. [Google Scholar]

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