M
Marc E. Pfetsch
Researcher at Technische Universität Darmstadt
Publications - 156
Citations - 3926
Marc E. Pfetsch is an academic researcher from Technische Universität Darmstadt. The author has contributed to research in topics: Polytope & Integer programming. The author has an hindex of 29, co-authored 146 publications receiving 3294 citations. Previous affiliations of Marc E. Pfetsch include Braunschweig University of Technology & Zuse Institute Berlin.
Papers
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Proceedings Article
Joint Antenna Selection and Phase-only Beamforming using Mixed-Integer Nonlinear Programming
Tobias Fischer,Ganapati Hegde,Frederic Matter,Marius Pesavento,Marc E. Pfetsch,Andreas M. Tillmann +5 more
TL;DR: In this article, the problem of joint antenna selection and beamforming design in downlink single-group multicast networks is formulated as an l0 minimization problem and a branch-and-cut based algorithm is proposed to solve the resulting mixed-integer nonlinear program to optimality.
Journal ArticleDOI
Identification of model uncertainty via optimal design of experiments applied to a mechanical press
Tristan Gally,Peter Groche,Florian Hoppe,Anja Kuttich,Alexander Matei,Marc E. Pfetsch,Martin Rakowitsch,Stefan Ulbrich +7 more
TL;DR: It is claimed that inconsistencies in the estimated parameter values, considering their approximated confidence ellipsoids as well, cannot be explained by data uncertainty but are indicators of model uncertainty.
Angebotsplanung im öffentlichen Nahverkehr
TL;DR: In this article, two optimierungsmodelle zur Linien-and Preisplanung vor Mathematical Optimization (MOI) have been proposed for offentlichen Nahverkehr, i.e., the Aufgaben der Netz-, Linien-, Fahr, and Preis planning.
Journal ArticleDOI
Irreducible Infeasible Subsystems of Semidefinite Systems
TL;DR: In this article, it was shown that the index sets of irreducible infeasible subsystems are exactly the supports of the vertices of the corresponding alternative polyhedron.
Book ChapterDOI
Competitive online multicommodity routing
TL;DR: This paper discusses a greedy online algorithm that routes each commodity by minimizing a convex cost function that only depends on the demands previously routed, and presents a competitive analysis of this algorithm showing that for affine linear price functions this algorithm is competitive.