Optimization Bounds from the Branching Dual

References

• Achterberg T (2007) Constraint integer programming. PhD thesis, Technische Universität, Berlin.Google Scholar
• Achterberg T, Koch T, Martin A (2005) Branching rules revisited. Oper. Res. Lett. 33(1):42–54.Crossref, Google Scholar
• Alvarez AM, Louveaux Q, Wehenkel L (2017) A machine learning-based approximation of strong branching. INFORMS J. Comput. 29(1):185–195.Link, Google Scholar
• Applegate DL, Bixby RE, Chvátal V, Cook WJ (2007) The Traveling Salesman Problem: A Computational Study (Princeton University Press, Princeton, NJ).Google Scholar
• Benichou M, Gautier JM, Girodet P, Hentges G, Ribiere R, Vincent O (1971) Experiments in mixed-integer linear programming. Math. Programming 1(1):76–94.Crossref, Google Scholar
• Bixby RE, Cook W, Cox A, Lee EK (1995) Parallel mixed integer programming. Technical Report CRPC-TR95554, Center for Research on Parallel Computation, Rice University, Houston.Google Scholar
• Blum A, Konjevodand G, Ravi R, Vempala S (1998) Semi-definite relaxations for minimum bandwidth and other vertex-ordering problems. Proc. 30th Annual ACM Sympos. Theory Comput. (Association for Computing Machinery, New York), 100–105.Crossref, Google Scholar
• Caprara A, Salazar-González JJ (2005) Laying out sparse graphs with provably minimum bandwidth. INFORMS J. Comput. 17(3):356–373.Link, Google Scholar
• Caprara A, Letchford AN, Salazar-González JJ (2011) Decorous lower bounds for minimum linear arrangement. INFORMS J. Comput. 23(1):26–40.Link, Google Scholar
• Chvátal V (1970) A remark on a problem of Harary. Czechoslovak Math. J. 20(1):109–111.Crossref, Google Scholar
• Dawande M, Hooker JN (2000) Inference-based sensitivity analysis for mixed integer/linear programming. Oper. Res. 48(4):623–634.Link, Google Scholar
• Gautier JM, Ribier R (1977) Experiments in mixed-integer linear programming using pseudo-costs. Math. Programming 12(1):26–47.Crossref, Google Scholar
• Harvey WD, Ginsberg ML (1995) Limited discrepancy search. Proc. 14th Internat. Joint Conf. Artificial Intelligence, vol. 1 (Morgan Kaufmann, San Francisco), 607–615.Google Scholar
• Hooker JN (1996) Inference duality as a basis for sensitivity analysis. Freuder EC, ed. Principles and Practice of Constraint Programming—CP96, Lecture Notes in Computer Science, vol. 1118 (Springer, Berlin), 224–236.Crossref, Google Scholar
• Hooker JN (2012) Integrated Methods for Optimization, 2nd ed. (Springer, New York).Crossref, Google Scholar
• Khalil EB, Bodic PL, Song L, Nemhauser G, Dilkina B (2016) Learning to branch in mixed integer programming. Proc. 13th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 724–731.Crossref, Google Scholar
• Korf RE (1985) Depth-first iterative-deepening: An optimal admissible tree search. Artificial Intelligence 27(1):97–109.Crossref, Google Scholar
• Linderoth JT, Savelsbergh MWP (1999) A computational study of search strategies for mixed integer programming. INFORMS J. Comput. 11(2):173–187.Link, Google Scholar
• Ostrowski J, Linderoth J, Rossi F, Smriglio S (2011) Orbital branching. Math. Programming 126(1):147–178.Crossref, Google Scholar
• Schulte C, Tack G, Lagerkvist MZ (2017) Modeling and programming with Gecode. User documentation.Google Scholar
• Turner JS (1986) On the probable performance of heuristics for bandwidth minimization. SIAM J. Comput. 15(2):561–580.Crossref, Google Scholar
• Vilím P, Laborie P, Shaw P (2015) Failure-directed search for constraint-based scheduling. Michel L, ed. Integration of AI and OR Techniques in Constraint Programming—CPAIOR 2015, Lecture Notes in Computer Science, vol. 9075 (Springer, New York), 437–453.Crossref, Google Scholar