4 citations · 8 across the 5 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
A Non-Convex Relaxation for Fixed-Rank Approximation
Carl Olsson, Marcus Carlsson, Erik Bylow
This paper considers the problem of finding a low rank matrix from observations of linear combinations of its elements. It is well known that if the problem fulfills a restricted i…