The main contents of this paper is two-fold. First, we present a method to approximate multivariate convex functions by piecewise linear upper and lower bounds. We consider a method that is based on function evaluations only. However, to use this method, the data have to be convex. Unfortunately, even if the underlying function is convex, this is not always the case due to (numerical) errors. Therefore, secondly, we present a multivariate data-smoothing method that smooths nonconvex data. We consider both the case that we have only function evaluations and the case that we also have derivative information. Furthermore, we show that our methods are polynomial time methods. We illustrate this methodology by applying it to some examples. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Siem, A. Y. D., Den Hertog, D., & Hoffmann, A. L. (2006). Multivariate convex approximation and least-norm convex data-smoothing. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3982 LNCS, pp. 812–821). Springer Verlag. https://doi.org/10.1007/11751595_86
Mendeley helps you to discover research relevant for your work.