Quadratic maximization and semidefinite relaxation

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 ${\cal NP}$-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.