Journal of Global Optimization

, Volume 67, Issue 4, pp 759–786 | Cite as

Monomial-wise optimal separable underestimators for mixed-integer polynomial optimization

  • Christoph Buchheim
  • Claudia D’Ambrosio


We introduce a new method for solving box-constrained mixed-integer polynomial problems to global optimality. The approach, a specialized branch-and-bound algorithm, is based on the computation of lower bounds provided by the minimization of separable underestimators of the polynomial objective function. The underestimators are the novelty of the approach because the standard approaches in global optimization are based on convex relaxations. Thanks to the fact that only simple bound constraints are present, minimizing the separable underestimator can be done very quickly. The underestimators are computed monomial-wise after the original polynomial has been shifted. We show that such valid underestimators exist and their degree can be bounded when the degree of the polynomial objective function is bounded, too. For the quartic case, all optimal monomial underestimators are determined analytically. We implemented and tested the branch-and-bound algorithm where these underestimators are hardcoded. The comparison against standard global optimization and polynomial optimization solvers clearly shows that our method outperforms the others, the only exception being the binary case where, though, it is still competitive. Moreover, our branch-and-bound approach suffers less in case of dense polynomial objective function, i.e., in case of polynomials having a large number of monomials. This paper is an extended and revised version of the preliminary paper [4].


Polynomial optimization Mixed-integer nonlinear programming Separable underestimators 



The work is partially supported by the EU grant FP7-PEOPLE-2012—ITN No. 316647 “Mixed-Integer Nonlinear Optimization”. The second author gratefully acknowledges the partial financial support under Grant ANR 12-JS02-009-01 “ATOMIC”.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Fakultät für MathematikTU DortmundDortmundGermany
  2. 2.LIX CNRS (UMR7161)École PolytechniquePalaiseau CedexFrance

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