This paper describes a new approach to multivariate scattered data smoothing. It is assumed that the data are generated by a Lipschitz continuous function f, and include random noise to be filtered out. The proposed approach uses known, or estimated value of the Lipschitz constant of f, and forces the data to be consistent with the Lipschitz properties of f. Depending on the assumptions about the distribution of the random noise, smoothing is reduced to a standard quadratic or a linear programming problem. We discuss an efficient algorithm which eliminates the redundant inequality constraints. Numerical experiments illustrate applicability and efficiency of the method. This approach provides an efficient new tool of multivariate scattered data approximation.
This is an electronic version of an article published in Optimization methods and software, vol. 22, no. 6, pp. 901-916. Optimization Methods and Software is available online at: http://www.informaworld.com/openurl?genre=article&issn=1055-6788&volume=22&issue=6&spage=901