The ALOP RTG Team
Overview over the ALOP RTG members and associates.
Copositive Optimization in Infinite Dimensional Spaces, Generalization of the Copositive and Completely Positive Cone, Combination of Conic Convex Optimization and Functional Analysis, Application: Optimization Problems on Infinite Graphs
Completely Positive Matrix Factorizations, Nonnegative Matrix Factorizations and Alternating Projection. These topics are especially connected to matrix theory, combinatorial optimization, quadratic optimization, completely positive optimization and second order cone programming. He successfully defended his thesis and now holds a Post-Doctoral position at the University of Augsburg.
Christina Müller studied Business Mathematics at Ulm University and Trier University. After receiving her M.Sc. in 2014 she started working as a scientific assistant at the Department of Biogeography at Trier University. Her Phd project is a joint work between the research group of Prof. Dr. Sachs (Numerics) and the research group of Prof. Dr. Veith (Biogeography). In 2016 she joined the RTG on Algorithmic Optimization as an Associate Member.
She successfully defended her thesis in January 2018.
Alumni, ALOP Associate
Multivariate Optimal Allocation in Survey Statistics, Calibration in Survey Statistics, Balanced Sampling, Multi-Criteria Optimization, Numerical Optimization.
Christina Schenk received her Doctorate in Natural Sciences in 2018. Having served as a scientific assistant in the research group of Prof. Dr. Volker Schulz on PDE-Constrained Optimization at Trier University since 2013, Ms. Schenk became an associate member of the research training group on Algorithmic Optimization in April 2016. At the present time she is a Postdoctoral Fellow at Carnegie Mellon University in Pittsburgh, Pennsylvania, USA.
Optimal Control Theory, Partial Integro Differential Equations on Bounded and Unbounded Spatial Domains, Neoclassical Growth Theory, Spatial Economics
Alumni, Scientific Researcher
- Numerical Optimization and Modelling
- Large Scale Optimization Algorithms
- Small Area Estimation.
Alumni, Post-doctoral Researcher, Associate Member
– Shape optimization
– Constrained optimization problems, in particular, constraints in the form of partial differential equations or variational inequalities
– Shape spaces as Riemannian manifolds and diffeological spaces
– Numerical methods