Friedrich Eisenbrand

Expertise Combinatorial Optimization, Algorithms and Complexity Current work Algorithms and complexity for Real Time Scheduling Mixed integer programming over TU systems Complexity of variants of Diophantine Approximation Combinatorial Geometry Friedrich Eisenbrand's research is supported by the Swiss National Science Foundation. Friedrich Eisenbrand's main research interests lie in the field of discrete optimization, in particular in algorithms and complexity, integer programming, geometry of numbers, and applied optimization. He is best known for his work on efficient algorithms for integer programming in fixed dimension and the theory of cutting planes, which are an important tool to solve large scale industrial optimization problems in practice.Before joining EPFL in March 2008, Friedrich Eisenbrand was a full professor of mathematics at the University of Paderborn. Friedrich received the Heinz Maier-Leibnitz award of the German Research Foundation (DFG) in 2004 and the Otto Hahn medal of the Max Planck Society in 2001. Teaching & PhD PhD Students Ruben Manuel Skorupinski, Neta Singer, Lukas Vogl, Jiaye Wei Past EPFL PhD Students Thomas Rothvoss, Nicolai Hähnle, Martin Niemeier, Adrian Aloysius Bock, Carsten Moldenhauer, Alfonso Bolívar Cevallos Manzano, Manuel Francesco Aprile, Igor Malinovic, Christoph Hunkenschröder, Moritz Andreas Venzin, Jana Tabea Cslovjecsek Past EPFL PhD Students as codirector Puck van Gerwen Courses Advanced linear algebra II - diagonalization MATH-115(a) The purpose of the course is to introduce the basic notions of linear algebra and to prove rigorously the main results of the subject. Discrete optimization MATH-261 This course is an introduction to linear and discrete optimization. Warning: This is a mathematics course! While much of the course will be algorithmic in nature, you will still need to be able to prove theorems. Linear Algebra Methods in Combinatorics MATH-672 The course will provide the students the skills to use simple notions in linear algebra such as rank, dimension, vector space, eigen values,tensor product, and matrices to solve seemingly accessible problems that may be quite natural and "elementary" and yet are difficult to solve by other methods.