We present a linearization strategy for mixed 0-1 quadratic programs that produces small formulations with tight relaxations. It combines constructs from a classical method of Glover and a more recent reformulation-linearization technique (RLT). By using binary identities to rewrite the objective, a variant of the first method results in a concise formulation with the level-1 RLT strength. This variant is achieved as a modified surrogate dual of a Lagrangian subproblem to the RLT. Special structures can be exploited to obtain reductions in problem size, without forfeiting strength. Preliminary computational experience demonstrates the potential of the new representations. © 2004 Elsevier B.V. All rights reserved.
Adams, W. P., Forrester, R. J., & Glover, F. W. (2004). Comparisons and enhancement strategies for linearizing mixed 0-1 quadratic programs. Discrete Optimization, 1(2), 99–120. https://doi.org/10.1016/j.disopt.2004.03.006