A Matrix-Free Multigrid Preconditioner for Spline Smoothing

Data smoothing with penalized splines is a popular method and is well established for one- or two-dimensional covariates. The extension to multiple covariates is straightforward but suffers from exponentially increasing memory requirements and computational complexity. This toolbox provides a matrix-free implementation of a conjugate gradient (CG) method for the regularized least squares problem resulting from tensor product B-spline smoothing with multivariate and scattered data. It further provides matrix-free preconditioned versions of the CG-algorithm where the user can choose between a simpler diagonal preconditioner and an advanced geometric multigrid preconditioner. The main advantage is that all algorithms are performed matrix-free and therefore require only a small amount of memory. For further detail see Siebenborn & Wagner (2019) .


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.1 by Martin Siebenborn, 10 days ago


Report a bug at https://github.com/SplineSmoothing/MGSS

Browse source code at https://github.com/cran/mgss

Authors: Martin Siebenborn [aut, cre, cph] , Julian Wagner [aut, cph]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports Rcpp, combinat, statmod, rTensor, Matrix

Suggests testthat

Linking to Rcpp

See at CRAN