Axel Ringh
My research interests are in the intersection of applied mathematics and areas such as control theory, signal processing, inverse problems, and machine learning. In particular, I am interested in computational optimal transport, especially the Sinkhorn iterations and its connections to other areas, and applications of optimal transport. The latter includes problems in formation control, state estimation for ensembles, dynamic flow problems, and various applications in machine learning.
Showing 9 publications
Inverse optimal control for averaged cost per stage linear quadratic regulators
Scalable Computation of Dynamic Flow Problems via Multimarginal Graph-Structured Optimal Transport
Mean field type control with species dependent dynamics via structured tensor optimization
Gain and phase type multipliers for structured feedback robustness
Graph-structured tensor optimization for nonlinear density control and mean field games
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Showing 1 research projects
New conditions for optimized freight transport solutions