Journal of Global Optimization

, Volume 61, Issue 3, pp 429–457 | Cite as

Branching and bounding improvements for global optimization algorithms with Lipschitz continuity properties

  • Coralia Cartis
  • Jaroslav M. FowkesEmail author
  • Nicholas I. M. Gould


We present improvements to branch and bound techniques for globally optimizing functions with Lipschitz continuity properties by developing novel bounding procedures and parallelisation strategies. The bounding procedures involve nonconvex quadratic or cubic lower bounds on the objective and use estimates of the spectrum of the Hessian or derivative tensor, respectively. As the nonconvex lower bounds are only tractable if solved over Euclidean balls, we implement them in the context of a recent branch and bound algorithm (Fowkes et al. in J Glob Optim 56:1791–1815, 2013) that uses overlapping balls. Compared to the rectangular tessellations of traditional branch and bound, overlapping ball coverings result in an increased number of subproblems that need to be solved and hence makes the need for their parallelization even more stringent and challenging. We develop parallel variants based on both data- and task-parallel paradigms, which we test on an HPC cluster on standard test problems with promising results.


Global optimization Lipschitzian optimization Parallel branch and bound Nonconvex programming 



The work of the first and second authors was supported by EPSRC grants EP/I028854/1 and NAIS EP/G036136/1 and the work of the third author by EP/I013067/1. We are also grateful to NAIS for funding computing time.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Coralia Cartis
    • 1
  • Jaroslav M. Fowkes
    • 1
    Email author
  • Nicholas I. M. Gould
    • 2
  1. 1.School of MathematicsUniversity of EdinburghEdinburghScotland, UK
  2. 2.Computational Science and Engineering DepartmentRutherford Appleton LaboratoryOxfordshireUK

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