4 citations · 6 across the 5 of their papers we have counts for
12 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…
On the Tightness of Semidefinite Relaxations for Rotation Estimation
Lucas Brynte, Viktor Larsson, José Pedro Iglesias +2
Why is it that semidefinite relaxations have been so successful in numerous applications in computer vision and robotics for solving non-convex optimization problems involving rota…
Monocular Depth Parameterizing Networks
Patrik Persson, Linn Öström, Carl Olsson
Monocular depth estimation is a highly challenging problem that is often addressed with deep neural networks. While these are able to use recognition of image features to predict r…
Accurate Optimization of Weighted Nuclear Norm for Non-Rigid Structure from Motion
José Pedro Iglesias, Carl Olsson, Marcus Valtonen Örnhag
Fitting a matrix of a given rank to data in a least squares sense can be done very effectively using 2nd order methods such as Levenberg-Marquardt by explicitly optimizing over a b…
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…