Abstract
A simplex, the convex hull of a set of \(n+1\) affinely independent points, is a useful tool in derivative-free optimization. The term uniform simplex was used by Audet and Hare (Derivative-free and blackbox optimization. Springer series in operations research and financial engineering, Springer, Cham, 2017). The purpose of this paper is to provide a framework for constructing a uniform simplex in \(\mathbb {R}^n\), which can then be aligned with a given vector. We prove that a uniform simplex has the greatest normalized volume of any simplex. Moreover, we show how to create a uniform minimal positive basis from a uniform simplex.
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Acknowledgements
We would like to acknowledge Dr. Warren Hare for his valuable and constructive suggestions. Part of the research was performed in the Computer-Aided Convex Analysis (CA2) laboratory funded by a Leaders Opportunity Fund (LOF, John R. Evans Leaders Fund—Funding for research infrastructure) from the Canada Foundation for Innovation (CFI) and by a British Columbia Knowledge Development Fund (BCKDF). Jarry-Bolduc’s research is partially funded by the Natural Sciences and Engineering Research Council (NSERC) of Canada, Discover Grant #2018-03865. Nadeau’s research is partially funded by NSERC of Canada, Discover Grant #2018-03865. Singh’s research is partially funded by NSERC of Canada, Discover Grant #298145-2013 and #2018-03928 (Lucet), and MITACS.
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Jarry-Bolduc, G., Nadeau, P. & Singh, S. Uniform simplex of an arbitrary orientation. Optim Lett 14, 1407–1417 (2020). https://doi.org/10.1007/s11590-019-01448-3
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DOI: https://doi.org/10.1007/s11590-019-01448-3