48 citations · 303 across the 37 of their papers we have counts for
38 papers
Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces
Martin Ryner, Jan Kronqvist, Johan Karlsson
This paper presents a framework for computing the Gromov-Wasserstein problem between two sets of points in low dimensional spaces, where the discrepancy is the squared Euclidean no…
Mean field type control with species dependent dynamics via structured tensor optimization
Axel Ringh, Isabel Haasler, Yongxin Chen +1
In this work we consider mean field type control problems with multiple species that have different dynamics. We formulate the discretized problem using a new type of entropy-regul…
Orthogonalization of data via Gromov-Wasserstein type feedback for clustering and visualization
Martin Ryner, Johan Karlsson
In this paper we propose an adaptive approach for clustering and visualization of data by an orthogonalization process. Starting with the data points being represented by a Markov…
Graph-structured tensor optimization for nonlinear density control and mean field games
Axel Ringh, Isabel Haasler, Yongxin Chen +1
In this work we develop a numerical method for solving a type of convex graph-structured tensor optimization problems. This type of problems, which can be seen as a generalization…
Quantifying and Computing Covariance Uncertainty
Filip Elvander, Johan Karlsson, Toon van Waterschoot
In this work, we consider the problem of bounding the values of a covariance function corresponding to a continuous-time stationary stochastic process or signal. Specifically, for…
On the complexity of the optimal transport problem with graph-structured cost
Jiaojiao Fan, Isabel Haasler, Johan Karlsson +1
Multi-marginal optimal transport (MOT) is a generalization of optimal transport to multiple marginals. Optimal transport has evolved into an important tool in many machine learning…
Scalable computation of dynamic flow problems via multi-marginal graph-structured optimal transport
Isabel Haasler, Axel Ringh, Yongxin Chen +1
In this work, we develop a new framework for dynamic network flow problems based on optimal transport theory. We show that the dynamic multi-commodity minimum-cost network flow pro…
Mixed-Spectrum Signals -- Discrete Approximations and Variance Expressions for Covariance Estimates
Filip Elvander, Johan Karlsson
The estimation of the covariance function of a stochastic process, or signal, is of integral importance for a multitude of signal processing applications. In this work, we derive c…
On analytic interpolation with non-classical constraints for solving problems in robust control
Axel Ringh, Johan Karlsson, Anders Lindquist
In this work we consider robust stabilization of uncertain dynamical systems and show that this can be achieved by solving a non-classically constrained analytic interpolation prob…
Incremental inference of collective graphical models
Rahul Singh, Isabel Haasler, Qinsheng Zhang +2
We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chai…
Multi-marginal optimal transport and probabilistic graphical models
Isabel Haasler, Rahul Singh, Qinsheng Zhang +2
We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for…
M-Spectral Estimation: A Flexible Approach Ensuring Rational Solutions
Bin Zhu, Augusto Ferrante, Johan Karlsson +1
This paper concerns a spectral estimation problem for multivariate (i.e., vector-valued) signals defined on a multidimensional domain, abbreviated as M. The problem is posed as…
Multi-marginal Optimal Transport with a Tree-structured cost and the Schrödinger Bridge Problem
Isabel Haasler, Axel Ringh, Yongxin Chen +1
The optimal transport problem has recently developed into a powerful framework for various applications in estimation and control. Many of the recent advances in the theory and app…
Inference with Aggregate Data: An Optimal Transport Approach
Rahul Singh, Isabel Haasler, Qinsheng Zhang +2
We consider inference (filtering) problems over probabilistic graphical models with aggregate data generated by a large population of individuals. We propose a new efficient belief…
An analytic interpolation approach to stability margins with emphasis on time delay
Axel Ringh, Johan Karlsson, Anders Lindquist
Unlike the situation with gain and phase margins in robust stabilization, the problem to determine an exact maximum delay margin is still an open problem, although extensive work h…
Fusion of Sensors Data in Automotive Radar Systems: A Spectral Estimation Approach
Bin Zhu, Augusto Ferrante, Johan Karlsson +1
To accurately estimate locations and velocities of surrounding targets (cars) is crucial for advanced driver assistance systems based on radar sensors. In this paper we derive meth…
M-Spectral Estimation: A Relative Entropy Approach
Bin Zhu, Augusto Ferrante, Johan Karlsson +1
This paper deals with M-signals, namely multivariate (or vector-valued) signals defined over a multidimensional domain. In particular, we propose an optimization technique to s…
Multi-Marginal Optimal Mass Transport with Partial Information
Filip Elvander, Isabel Haasler, Andreas Jakobsson +1
During recent decades, there has been a substantial development in optimal mass transport theory and methods. In this work, we consider multi-marginal problems wherein only partial…
Estimating ensemble flows on a hidden Markov chain
Isabel Haasler, Axel Ringh, Yongxin Chen +1
We propose a new framework to estimate the evolution of an ensemble of indistinguishable agents on a hidden Markov chain using only aggregate output data. This work can be viewed a…
Automatic Target Recognition Using Discrimination Based on Optimal Transport
Ali Sadeghian, Deoksu Lim, Johan Karlsson +1
The use of distances based on optimal transportation has recently shown promise for discrimination of power spectra. In particular, spectral estimation methods based on l1 regulari…
Non-Coherent Sensor Fusion via Entropy Regularized Optimal Mass Transport
Filip Elvander, Isabel Haasler, Andreas Jakobsson +1
This work presents a method for information fusion in source localization applications. The method utilizes the concept of optimal mass transport in order to construct estimates of…
Data-driven nonsmooth optimization
Sebastian Banert, Axel Ringh, Jonas Adler +2
In this work, we consider methods for solving large-scale optimization problems with a possibly nonsmooth objective function. The key idea is to first specify a class of optimizati…
Modeling collective behaviors: A moment-based approach
Silun Zhang, Axel Ringh, Xiaoming Hu +1
In this work we introduce an approach for modeling and analyzing collective behavior of a group of agents using moments. We represent the group of agents via their distribution and…
State tracking of linear ensembles via optimal mass transport
Yongxin Chen, Johan Karlsson
We consider the problems of tracking an ensemble of indistinguishable agents with linear dynamics based only on output measurements. In this setting, the dynamics of the agents can…