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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Computing 52 (1994), S. 123-137 
    ISSN: 1436-5057
    Keywords: 90C06 ; 90C27 ; Max-cut problem ; semidefinite programming ; min-max eigenvalue problem
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Description / Table of Contents: Zusammenfassung Es wird eine obere Schranke für das Max-Cut Problem untersucht, die sich aus einer Relaxation des diskreten Problems zu einem stetigen, nichtlinearen konvexen Problem ergibt. Die Relaxation ist polynomial lösbar. Es werden die Grenzen des Ansatzes unter dem Einsatz fortgeschrittener Methoden aus numerischer linearer Algebra und nichtglatter Optimierung untersucht. Verschiedene Graphenklassen mit bis zu 50 000 Knoten und 4 Millionen Kanten werden mit dem Ansatz behandelt. Da die theoretische obere Schranke in der Praxis nur mit einer gewissen Genauigkeit bestimmt werden kann, wird ein Dualitätsmodell zwischen knoten- und kantenorientierten Relaxationen verwendet, um den Unterschied zwischen der theoretischen und der berechneten Schranke abzuschätzen.
    Notes: Abstract We study an upper bound on the max-cut problem defined via a relaxation of the discrete problem to a continuous nonlinear convex problem, which can be solved efficiently. We demonstrate how far the approach can be pushed using advanced techniques from numerical linear algebra and nonsmooth optimization. Various classes of graphs with up to 50 000 nodes and up to four million edges are considered. Since the theoretical bound can be computed only with a certain precision in practice, we use duality between node- and edge-oriented relaxations to estimate the difference between the theoretical and the computed bounds.
    Type of Medium: Electronic Resource
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Journal of global optimization 7 (1995), S. 51-73 
    ISSN: 1573-2916
    Keywords: Quadratic boolean programming ; semidefinite programming ; bounds ; Lagrangian duality ; parametric programming ; trust region subproblems ; minmax eigenvalue problems ; quadratic assignment problem ; graph partitioning ; max-clique ; theta function
    Source: Springer Online Journal Archives 1860-2000
    Topics: Mathematics
    Notes: Abstract We review various relaxations of (0,1)-quadratic programming problems. These include semidefinite programs, parametric trust region problems and concave quadratic maximization. All relaxations that we consider lead to efficiently solvable problems. The main contributions of the paper are the following. Using Lagrangian duality, we prove equivalence of the relaxations in a unified and simple way. Some of these equivalences have been known previously, but our approach leads to short and transparent proofs. Moreover we extend the approach to the case of equality constrained problems by taking the squared linear constraints into the objective function. We show how this technique can be applied to the Quadratic Assignment Problem, the Graph Partition Problem and the Max-Clique Problem. Finally we show our relaxation to be best possible among all quadratic majorants with zero trace.
    Type of Medium: Electronic Resource
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