Additive sparse grid fitting
Markus Hegland · 2003
Abstract. We propose an iterative algorithm for high-dimensional sparse grid regression with penalty. The algorithm is of additive Schwarz type and is thus parallel. We show that the convergence of the algorithm is better than additive Schwarz and examples demonstrate that convergence is between that of additive Schwarz and multiplicative Schwarz procedures. Similarly, the method shows improved performance compared to (additive) iterative methods based on the combination technique. Fitting data with complex models has been of some interest in machine learning and statistics, and more recently in analysis and computational mathematics [1,3,4,7,10]. Questions which have been discussed include the choice of the best function consistent with the data, relating