@article{,
    author = {Brieden, Andreas; Gritzmann, Peter},
    title = {On Clustering Bodies: Geometry and Polyhedral Approximation},
    editor = {},
    booktitle = {},
    series = {},
    journal = {Discrete & Computational Geometry},
    address = {},
    publisher = {},
    edition = {},
    year = {2010},
    isbn = {},
    volume = {44},
    number =  {3},
    pages = {508-534},
    url = {},
    doi = {10.1007/s00454-009-9226-7},
    keywords = {Computational convexity ; Optimization ; Geometric clustering ; Convex maximization ; Polynomial approximation ; Permutahedron},
    abstract = {The present paper studies certain classes of closed convex sets in finite-dimensional real spaces that are motivated by their application to convex maximization problems, most notably, those evolving from geometric clustering. While these optimization problems are ℕℙ-hard in general, polynomial-time approximation algorithms can be devised whenever appropriate polyhedral approximations of their related clustering bodies are available. Here we give various structural results that lead to tight approximations.},
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Wirtschafts- und Organisationswissenschaften, WOW 1 - Institut für Controlling, Finanz- und Risikomanagement, Professur: Brieden, Andreas},
    
    
}