Abstract
We describe how to deploy interval derivatives up to second order in the context of unconstrained global optimization with a branch and bound method. For computing these derivatives we combine the Boost interval library and the algorithmic differentiation tool dco/c++. The differentiation tool also computes the required floating-point derivatives for a local search algorithm that is embedded in our branch and bound implementation. First results are promising in terms of utility of interval adjoints in global optimization.
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Deussen, J., Naumann, U. (2020). Discrete Interval Adjoints in Unconstrained Global Optimization. In: Le Thi, H., Le, H., Pham Dinh, T. (eds) Optimization of Complex Systems: Theory, Models, Algorithms and Applications. WCGO 2019. Advances in Intelligent Systems and Computing, vol 991. Springer, Cham. https://doi.org/10.1007/978-3-030-21803-4_8
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DOI: https://doi.org/10.1007/978-3-030-21803-4_8
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