Constrained Optimization Methods for Models in Data Fusion 

In Data Fusion, one analyzes different measurements of the same phenomenon, which are stored in several matrices and/or tensors. The goal is to simultaneously factorize these datasets in order to allow for dimensionality reduction, clustering, feature extraction, prediction and the like. The factorizations are often subject to various constraints which model the type of data at hand. An inherent problem of these models is that they are notoriously hard to solve, especially if several constraints come in conjunction. The aim of this workshop is to learn about existing models and their applications, to study different optimization techniques, and to possibly improve them by introducing more solvers or accelerating the existing ones. One particular focus lies on manifold optimization, where many open problems remain. 

Registration

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List of Participants

• Evrim Acar,    SimulaMet

• Florian Becker,    SimulaMet

• Ronny Bergmann,    NTNU

• Pankaj Gautam,    NTNU

• Markus Grasmair,    NTNU

• Hajg Jasa,    NTNU

• Martin Ludvigsen,    NTNU

• Max Pfeffer,    SimulaMet

• Marie Roald,    SimulaMet

• Carla Schenker,    SimulaMet

• Shi Yan,    SimulaMet

• Lu Li,    SimulaMet