Tightening discretization-based MILP models for the pooling problem using upper bounds on bilinear terms

Yifu Chen, Christos T. Maravelias, Xiaomin Zhang

Research output: Contribution to journalArticlepeer-review


Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem. These methods convert the original nonconvex optimization problems into mixed-integer linear programs (MILPs). In this paper we study tightening methods for these MILP models for the pooling problem, and derive valid constraints using upper bounds on bilinear terms. Computational results demonstrate the effectiveness of our methods in terms of reducing solution time.

Original languageEnglish (US)
JournalOptimization Letters
StateAccepted/In press - 2023

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting (miscellaneous)
  • Control and Optimization


  • Binary expansion
  • Nonconvex optimization
  • Valid constraints

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