Quadratic maximization and semidefinite relaxation

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In this paper we study a class of quadratic maximization problems and their semidefinite programming (SDP) relaxation. For a special subclass of the problems we show that the SDP relaxation provides an exact optimal solution. Another subclass, which is script Nscript P-hard, guarantees that the SDP relaxation yields an approximate solution with a worst-case performance ratio of 0.87856.... This is a generalization of the well-known result of Goemans and Williamson for the maximum-cut problem. Finally, we discuss extensions of these results in the presence of a certain type of sign restrictions.

Original languageEnglish (US)
Pages (from-to)453-465
Number of pages13
JournalMathematical Programming, Series B
Issue number3
StatePublished - May 2000


  • Approximation
  • Polynomial-time algorithm
  • Quadratic programming
  • Semidefinite programming relaxation


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