Abstract
First steps toward a novel deterministic algorithm for finding a minimum among all local minima of a nonconvex objective over a given domain are discussed. Nonsmooth convex relaxations of the objective and of its gradient are optimized in the context of a global branch and bound method. While preliminary numerical results look promising further effort is required to fully integrate the method into a robust and computationally efficient software solution.
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Acknowledgements
This work was funded by Deutsche Forschungsgemeinschaft under grant number NA487/8-1. Further financial support was provided by the School of Simulation and Data Science at RWTH Aachen University.
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Deussen, J., Hüser, J., Naumann, U. (2020). Toward Global Search for Local Optima. In: Neufeld, J.S., Buscher, U., Lasch, R., Möst, D., Schönberger, J. (eds) Operations Research Proceedings 2019. Operations Research Proceedings. Springer, Cham. https://doi.org/10.1007/978-3-030-48439-2_12
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DOI: https://doi.org/10.1007/978-3-030-48439-2_12
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