15 citations · 29 across the 5 of their papers we have counts for
6 papers
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…
An unbiased approach to low rank recovery
Marcus Carlsson, Daniele Gerosa, Carl Olsson
Low rank recovery problems have been a subject of intense study in recent years. While the rank function is useful for regularization it is difficult to optimize due to its non-con…
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…
A Trace theorem for Martinet--type vector fields
Daniele Gerosa, Roberto Monti, Daniele Morbidelli
In we consider the vector fields \[ X_1 =\frac{ \partial }{\partial x},\qquad X_2 =\frac{ \partial }{\partial y}+ |x|^α\frac{ \partial }{\partial z}, \] where $α\in\…
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…