4 citations · 6 across the 5 of their papers we have counts for
6 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…
A Unified Optimization Framework for Low-Rank Inducing Penalties
Marcus Valtonen Örnhag, Carl Olsson, Anders Heyden
In this paper we study the convex envelopes of a new class of functions. Using this approach, we are able to unify two important classes of regularizers from unbiased non-convex fo…
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…
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…
Non-Convex Rank/Sparsity Regularization and Local Minima
Carl Olsson, Marcus Carlsson, Fredrik Andersson +1
This paper considers the problem of recovering either a low rank matrix or a sparse vector from observations of linear combinations of the vector or matrix elements. Recent methods…