4 papers · 1 filter
High-dimensional sparse recovery from function samples Decoders, guarantees and instance optimality
Moritz Moeller, Sebastian Neumayer, Kateryna Pozharska +2
We investigate the reconstruction of multivariate functions from samples using sparse recovery techniques. For Square Root Lasso, Orthogonal Matching Pursuit, and Compressive Sampl…
Sampling recovery in and other norms
David Krieg, Kateryna Pozharska, Mario Ullrich +1
We study the recovery of functions in various norms, including with , based on function evaluations. We obtain worst case error bounds for general classes of…
Exact discretization, tight frames and recovery via D-optimal designs
Felix Bartel, Lutz Kämmerer, Kateryna Pozharska +2
-optimal designs originate in statistics literature as an approach for optimal experimental designs. In numerical analysis points and weights resulting from maximal determinants…
High-dimensional sparse trigonometric approximation in the uniform norm and consequences for sampling recovery
Moritz Moeller, Serhii Stasyuk, Tino Ullrich
Recent findings by Jahn, T. Ullrich, Voigtlaender [14] relate non-linear sampling numbers for the square norm to quantities involving trigonometric best term approximation erro…