Dr. Ulf Friedrich

Otto von Guericke University Magdeburg, Germany

I am a member of the Institute of Mathematical Optimization at the Faculty of Mathematics of OVGU Magdeburg. I studied applied mathematics at Trier University and received my PhD in the Operations Research Group of Sven de Vries. As a postdoc, I joined the research training group Algorithmic Optimization in Trier and worked in the Operations Research Group of Andreas S. Schulz at the Technical University of Munich.


My research belongs to the broad field of mathematical optimization and is a fusion of practical problem solving and theoretical abstraction. Currently, I am working in two major areas: On the one hand, the application of MIP and MINLP techniques for the solution of application problems from data science or computational statistics and, on the other hand, the development of algorithms for IP that rely on techniques from complex analysis in several variables.

Map of Italy

Integer Optimization in Applied Statistics

The census conducted in Germany in 2011 was the major motivation to study computational statistics and, for example, stratified sampling problems. Here, a sample is drawn (for instance 10 percent of the population) which has to be distributed among the population in such a way that the overall variance is minimized and certain structural and legal constraints are respected. Solving optimal allocation problems and their generalizations is important in many data-driven applications. The map on the left illustrates an Italian business survey and is taken from a project with my colleagues Ralf Münnich and Martin Rupp.

Paths in the Unit Disc

Analytic Algorithms for IP

I study integer linear programming problems with the additional restriction that all input data has non-negative entries. For these problems, I have developed a novel solution approach which relies on results from the field of analysis in several complex variables. Both fields seem to be unrelated at first sight and the project connects pure mathematics and optimization theory in a beautiful way. In particular, the path independence of complex integrals can be used to improve the numerical performance of the method, as depicted in the picture. This research is part of my project An Analytic Computational Solver for Integer Programming funded by the Volkswagen Foundation in their Experiment! initiative.


Articles in Peer-Reviewed Journals

PhD Thesis

  • Discrete allocation in survey sampling and analytic algorithms for integer programming, Trier University, 2016. Reviewers: Sven de Vries (Trier University) and Alexander Martin (FAU Erlangen-Nürnberg Unversity).


Recent Conferences and Talks

In the last three years, I have presented my work at the following occasions:

  • Forum Experiment! 2021, Hannover, Germany
    (talk “An Analytic Computational Solver for Integer Programming”)
  • NTTS Conference 2021, online event
    (talk “Building a Geo-referenced Microsimulation Model with Discrete Optimization”)
  • Research Seminar of the Institute for Electrical Energy Storage Technology 2020, Technical University of Munich, Germany
    (talk “Integer Programming and its Applications”)
  • CMStatistics 2019, Senate House University of London, UK
    (talk “Integer Programming and Machine Learning for Computational Statistics”)
  • EURO 2019, University College Dublin, Ireland
    (talk “Optimal sampling under cardinality constraints”)
  • SMSA 2019, Technical University of Dresden, Germany
    (talk “Address selection by combinatorial optimization”)

Contact Information