27 citations · 138 across the 24 of their papers we have counts for
7 papers · 1 filter
Bias Reduction in Compressed Sensing
Carl Olsson, Marcus Carlsson, Daniele Gerosa
Sparsity and rank functions are important ways of regularizing under-determined linear systems. Optimization of the resulting formulations is made difficult since both these penalt…
On Convex Envelopes and Regularization of Non-Convex Functionals without moving Global Minima
Marcus Carlsson
We provide theory for the computation of convex envelopes of non-convex functionals including an l2-term, and use these to suggest a method for regularizing a more general set of p…
Perturbation theory for Hermitian matrix-functions based on vector-fields
Marcus Carlsson
We consider "spectral" matrix-functions for Hermitian matrices, where the novelty is that the function applied to the spectrum is allowed to be a vector-field rather than a scalar…
Perturbation theory for the matrix square root and matrix modulus
Marcus Carlsson
We provide first order perturbation formulas for the matrix square root (in the positive semi-definite case) and the matrix modulus (in the general case). The results are new for s…
Four-dimensional tomographic reconstruction by time domain decomposition
Viktor V. Nikitin, Marcus Carlsson, Fredrik Andersson +1
Since the beginnings of tomography, the requirement that the sample does not change during the acquisition of one tomographic rotation is unchanged. We derived and successfully imp…
Perturbation theory for the spectral decomposition of Hermitian matrices
Marcus Carlsson
Let A and E be Hermitian self-adjoint matrices, where A is fixed and E a small perturbation. We study how the eigenvalues and eigenvectors of A+E depend on E, with the aim of obtai…
An unbiased approach to compressed sensing
Marcus Carlsson, Daniele Gerosa, Carl Olsson
In compressed sensing a sparse vector is approximately retrieved from an under-determined equation system . Exact retrieval would mean solving a large combinatorial problem w…