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Discretization and global optimization for mixed integer bilinear programming

Author

Listed:
  • Xin Cheng

    (Queen’s University)

  • Xiang Li

    (Queen’s University)

Abstract

We consider global optimization of mixed-integer bilinear programs (MIBLP) using discretization-based mixed-integer linear programming (MILP) relaxations. We start from the widely used radix-based discretization formulation (called R-formulation in this paper), where the base R may be any natural number, but we do not require the discretization level to be a power of R. We prove the conditions under which R-formulation is locally sharp, and then propose an $$R^+$$ R + -formulation that is always locally sharp. We also propose an H-formulation that allows multiple bases and prove that it is also always locally sharp. We develop a global optimization algorithm with adaptive discretization (GOAD) where the discretization level of each variable is determined according to the solution of previously solved MILP relaxations. The computational study shows the computational advantage of GOAD over general-purpose global solvers BARON and SCIP.

Suggested Citation

  • Xin Cheng & Xiang Li, 2022. "Discretization and global optimization for mixed integer bilinear programming," Journal of Global Optimization, Springer, vol. 84(4), pages 843-867, December.
    • Handle: RePEc:spr:jglopt:v:84:y:2022:i:4:d:10.1007_s10898-022-01179-3
    DOI: 10.1007/s10898-022-01179-3
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    References listed on IDEAS

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    1. João Teles & Pedro Castro & Henrique Matos, 2013. "Multi-parametric disaggregation technique for global optimization of polynomial programming problems," Journal of Global Optimization, Springer, vol. 55(2), pages 227-251, February.
    2. Akshay Gupte & Shabbir Ahmed & Santanu S. Dey & Myun Seok Cheon, 2017. "Relaxations and discretizations for the pooling problem," Journal of Global Optimization, Springer, vol. 67(3), pages 631-669, March.
    3. Scott Kolodziej & Pedro Castro & Ignacio Grossmann, 2013. "Global optimization of bilinear programs with a multiparametric disaggregation technique," Journal of Global Optimization, Springer, vol. 57(4), pages 1039-1063, December.
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