Author

M. Multerer

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Preprint Sep 2026

$hp$-adaptive trees for graph signal approximation

Tree-encoded partitionings of graphs are fundamental tools for the decomposition and approximation of graph signals. For the efficient approximation of such graph signals, we develop strategies based on $hp$-refinement by combining domain decomposition with an improved local approximation using polynomials of higher de...

Giacomo Elefante, Wolfgang Erb, M. Multerer · 0 citations

Preprint Aug 2026

Samplet compression for conditionally positive definite kernels and universal Kriging

A samplet-based framework for the efficient numerical solution of saddle-point systems arising from conditionally positive definite (CPD) kernel approximation in general and universal Kriging in particular, which achieves O(N log N) cost for the assembly and the storage of the saddle-point system.

Sara Avesani, Rüdiger Kempf, M. Multerer et al. · 0 citations