Tighter McCormick relaxations through subgradient propagation

  • Jaromił Najman
  • Alexander MitsosEmail author


Tight convex and concave relaxations are of high importance in deterministic global optimization. We present a method to tighten relaxations obtained by the McCormick technique. We use the McCormick subgradient propagation (Mitsos et al. in SIAM J Optim 20(2):573–601, 2009) to construct simple affine under- and overestimators of each factor of the original factorable function. Then, we minimize and maximize these affine relaxations in order to obtain possibly improved range bounds for every factor resulting in possibly tighter final McCormick relaxations. We discuss the method and its limitations, in particular the lack of guarantee for improvement. Subsequently, we provide numerical results for benchmark cases found in the MINLPLib2 library and case studies presented in previous works, where the McCormick technique appears to be advantageous, and discuss computational efficiency. We see that the presented algorithm provides a significant improvement in tightness and decrease in computational time, especially in the case studies using the reduced space formulation presented in (Bongartz and Mitsos in J Glob Optim 69:761–796, 2017).


Global optimization McCormick Range reduction MAiNGO 

Mathematics Subject Classification

49M20 49M37 65K05 90C26 



We would like to thank Dr. Benoît Chachuat for providing MC++ v2.0 and Dominik Bongartz and Artur Schweidtmann for providing numerical case studies. We appreciate the thorough review and helpful comments provided by the anonymous reviewers and editors which resulted in an improved manuscript. This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Improved McCormick Relaxations for the efficient Global Optimization in the Space of Degrees of Freedom MA 1188/34-1.


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Authors and Affiliations

  1. 1.AVT - Aachener Verfahrenstechnik, Process Systems EngineeringRWTH Aachen UniversityAachenGermany

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