Publications (17)
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…
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…
A parallel framework for graphical optimal transport
Jiaojiao Fan, Isabel Haasler, Qinsheng Zhang +2
We study multi-marginal optimal transport (MOT) problems where the underlying cost has a graphical structure. These graphical multi-marginal optimal transport problems have found a…
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…
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…
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…