The error in multivariate linear extrapolation with applications to derivative-free optimization

We study in this paper the function approximation error of multivariate linear extrapolation. The sharp error bound of linear interpolation already exists in the literature. However, linear extrapolation is used far more often in applications such as derivative-free optimization, while its error is not well-studied. We introduce in this paper a method to numerically compute the sharp bound on the error, and then present several analytical bounds along with the conditions under which they are sharp. We also provide a complexity analysis of a basic simplicial search method to illustrate an application of these error bounds in derivative-free optimization. All results are under the assumptions that the function being interpolated has Lipschitz continuous gradient and is interpolated on an affinely independent sample set.