26 research outputs found
Sparse approximation of multivariate functions from small datasets via weighted orthogonal matching pursuit
We show the potential of greedy recovery strategies for the sparse
approximation of multivariate functions from a small dataset of pointwise
evaluations by considering an extension of the orthogonal matching pursuit to
the setting of weighted sparsity. The proposed recovery strategy is based on a
formal derivation of the greedy index selection rule. Numerical experiments
show that the proposed weighted orthogonal matching pursuit algorithm is able
to reach accuracy levels similar to those of weighted minimization
programs while considerably improving the computational efficiency for small
values of the sparsity level