no code implementations • 13 Feb 2020 • Martin Morin, Pontus Giselsson
Variance-reduced stochastic gradient methods have gained popularity in recent times.
no code implementations • 10 Jan 2020 • Emil Björnson, Pontus Giselsson
Deep learning has proved itself to be a powerful tool to develop data-driven signal processing algorithms for challenging engineering problems.
no code implementations • 21 Mar 2019 • Martin Morin, Pontus Giselsson
However, in the biased cases they still do not correspond well with practical experiences and we therefore examine the effect of bias numerically on a set of classification problems.
1 code implementation • 1 Dec 2018 • Ernest K. Ryu, Adrien B. Taylor, Carolina Bergeling, Pontus Giselsson
We propose a methodology for studying the performance of common splitting methods through semidefinite programming.
Optimization and Control 47H05 47H09 68Q25 90C22 90C25 90C30 90C60
2 code implementations • 17 Oct 2018 • Christian Grussler, Pontus Giselsson
This is known for the nuclear norm, but not for most other members of the low-rank inducing family.
2 code implementations • 11 Oct 2017 • Christian Grussler, Pontus Giselsson
We analyze the local convergence of proximal splitting algorithms to solve optimization problems that are convex besides a rank constraint.
2 code implementations • 9 Dec 2016 • Christian Grussler, Pontus Giselsson
A posteriori guarantees on solving an underlying rank constrained optimization problem with these convex relaxations are provided.
1 code implementation • 6 Jun 2016 • Christian Grussler, Anders Rantzer, Pontus Giselsson
In this paper, we propose an alternative convex relaxation that uses the convex envelope of the squared Frobenius norm and the rank constraint.