Arıkan, OrhanBurachik, R. S.Kaya, C. Y.2021-02-192021-02-1920200925-5001http://hdl.handle.net/11693/75476We introduce a new regularization technique, using what we refer to as the Steklov regularization function, and apply this technique to devise an algorithm that computes a global minimizer of univariate coercive functions. First, we show that the Steklov regularization convexifies a given univariate coercive function. Then, by using the regularization parameter as the independent variable, a trajectory is constructed on the surface generated by the Steklov function. For monic quartic polynomials, we prove that this trajectory does generate a global minimizer. In the process, we derive some properties of quartic polynomials. Comparisons are made with a previous approach which uses a quadratic regularization function. We carry out numerical experiments to illustrate the working of the new method on polynomials of various degree as well as a non-polynomial function.EnglishGlobal optimizationMean filterSteklov smoothingSteklov regularizationScale–shift invarianceTrajectory methodsArticle10.1007/s10898-019-00837-3