[Journal] Surveys in Operations Research and Management

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101] present a bounding procedure, called the roof dual, which replaces each quadratic function with a tight linear under-estimator. Extensions of this are surveyed in Boros and Hammer [102]. 102 S. N. Letchford / Surveys in Operations Research and Management Science 17 (2012) 97–106 • Pardalos and Rodgers [103] solve unconstrained 0–1 QPs within a branch-and-bound algorithm involving careful preprocessing and computational efficiencies. • Poljak and Wolkowicz [104] examine several bounding techniques for unconstrained 0–1 QPs, and show that they all give the same bounds.

Linderoth, A simplicial branch-and-bound algorithm for solving quadratically constrained quadratic programs, Math. Program. 103 (2005) 251–282. [45] A. Fischer, C. Helmberg, The symmetric quadratic traveling salesman problem, Manuscript, Fakultät für Mathematik, Technishe Universität Chemnitz, Germany, 2011. [46] I. Gentilini, F. Margot, K. Shimada, The traveling salesman problem with neighborhoods: MINLP solution. Manuscript, Tepper School of Business, Carnegie Mellon University. Optim. , 2011 (in press).

Hansen, B. Simeone, Roof duality, complementation and persistency in quadratic 0–1 optimization, Math. Program. 28 (1984) 121–155. [102] E. L. Hammer, Pseudo-Boolean optimization, Discrete Appl. Math. 123 (2002) 155–225. M. P. Rodgers, Computational aspects of a branch and bound algorithm for quadratic zero–one programming, Computing 45 (2) (1990) 131–144. [104] S. Poljak, H. Wolkowicz, Convex relaxations of (0, 1)-quadratic programming, Math. Oper. Res. 20 (1995) 550–561. [105] A.

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