Multivariate Convex Approximation and Least-Norm Convex Data-Smoothing

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.

Keywords: approximation theory; convexity; data-smoothing

JEL Classification: C60

Suggested Citation: Suggested Citation

Siem, A.Y.D. and den Hertog, Dick and Hoffmann, A. L., Multivariate Convex Approximation and Least-Norm Convex Data-Smoothing (December 2005). CentER Discussion Paper Series No. 2005-132, Available at SSRN: https://ssrn.com/abstract=875615 or http://dx.doi.org/10.2139/ssrn.875615