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Nonconvex sensitivity-based generalized Benders decomposition

Author

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  • Jia-Jiang Lin

    (China University of Petroleum Beijing)

  • Feng Xu

    (China University of Petroleum Beijing)

  • Xiong-Lin Luo

    (China University of Petroleum Beijing)

Abstract

This paper considers general separable pseudoconvex optimization problems with continuous complicating variables in which primal and projected problems are both pseudoconvex problems. A novel decomposition method based on generalized Benders decomposition, named nonconvex sensitivity-based generalized Benders decomposition, is developed and proved strictly to obtain optimal solutions of general separable pseudoconvex optimization problems of interest without constructing surrogate models. By the use of a reformulation strategy (introducing an extra equality constraint and constructing several subproblems), the algorithm handles the nonconvexity by direct manipulations of consistent linear Benders cuts and the check of optimality conditions and approximating the feasible region of complicating variables by supporting hyperplanes. The master problems of the new algorithm are always linear programming problems and the solution of the algorithm contains sensitivity information about complicating variables. Moreover, the new algorithm could also be used as a tool to check the nonconvexity of an optimization problem. Two cases are given to confirm the validity and applicability of the proposed algorithm.

Suggested Citation

  • Jia-Jiang Lin & Feng Xu & Xiong-Lin Luo, 2023. "Nonconvex sensitivity-based generalized Benders decomposition," Journal of Global Optimization, Springer, vol. 86(1), pages 37-60, May.
    • Handle: RePEc:spr:jglopt:v:86:y:2023:i:1:d:10.1007_s10898-022-01254-9
    DOI: 10.1007/s10898-022-01254-9
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    References listed on IDEAS

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    1. Xiang Li & Asgeir Tomasgard & Paul I. Barton, 2011. "Nonconvex Generalized Benders Decomposition for Stochastic Separable Mixed-Integer Nonlinear Programs," Journal of Optimization Theory and Applications, Springer, vol. 151(3), pages 425-454, December.
    2. Can Li & Ignacio E. Grossmann, 2019. "A generalized Benders decomposition-based branch and cut algorithm for two-stage stochastic programs with nonconvex constraints and mixed-binary first and second stage variables," Journal of Global Optimization, Springer, vol. 75(2), pages 247-272, October.
    3. Emmanuel Ogbe & Xiang Li, 2019. "A joint decomposition method for global optimization of multiscenario nonconvex mixed-integer nonlinear programs," Journal of Global Optimization, Springer, vol. 75(3), pages 595-629, November.
    4. Zhou Wei & M. Montaz Ali, 2015. "Outer Approximation Algorithm for One Class of Convex Mixed-Integer Nonlinear Programming Problems with Partial Differentiability," Journal of Optimization Theory and Applications, Springer, vol. 167(2), pages 644-652, November.
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    Cited by:

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