activity
20122021
most citedBias Reduction in Compressed Sensing

4 citations · 8 across the 5 of their papers we have counts for

collaborators

14 papers

math.OC2021

Relaxations for Non-Separable Cardinality/Rank Penalties

Carl Olsson, Daniele Gerosa, Marcus Carlsson

Rank and cardinality penalties are hard to handle in optimization frameworks due to non-convexity and discontinuity. Strong approximations have been a subject of intense study and…

physics.geo-ph2019

Numerical simulations of NMR relaxation in chalk using local Robin boundary conditions

M. Ogren, D. Jha, S. Dobberschutz +5

The interpretation of nuclear magnetic resonance (NMR) data is of interest in a number of fields. In Ögren [Eur. Phys. J. B (2014) 87: 255] local boundary conditions for random wal…

math.OC2019

On phase retrieval via matrix completion and the estimation of low rank PSD matrices

Marcus Carlsson, Daniele Gerosa

Given underdetermined measurements of a Positive Semi-Definite (PSD) matrix of known low rank , we present a new algorithm to estimate based on recent advances in non-co…

math.OC20184 cited

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…

math.OC2018

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…

math.FA2018

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…