Abstract
Convexity is an important property in nonlinear optimization since it allows to apply efficient local methods for finding global solutions. We propose to apply symbolic methods to prove or disprove convexity of rational functions over a polyhedral domain. Our algorithms reduce convexity questions to real quantifier elimination problems. Our methods are implemented and publicly available in the open source computer algebra system Reduce. Our long term goal is to integrate Reduce as a “workhorse” for symbolic computations into a numerical solver.
This work was supported by the DFG Research Center Matheon Mathematics for key technologies in Berlin, http://www.matheon.de
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Neun, W., Sturm, T., Vigerske, S. (2010). Supporting Global Numerical Optimization of Rational Functions by Generic Symbolic Convexity Tests . In: Gerdt, V.P., Koepf, W., Mayr, E.W., Vorozhtsov, E.V. (eds) Computer Algebra in Scientific Computing. CASC 2010. Lecture Notes in Computer Science, vol 6244. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15274-0_19
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