Quadratic programming with one negative eigenvalue is NP-hard Authors
Received: 06 February 1991 DOI:
Cite this article as: Pardalos, P.M. & Vavasis, S.A. J Glob Optim (1991) 1: 15. doi:10.1007/BF00120662 Abstract
We show that the problem of minimizing a concave quadratic function with one concave direction is NP-hard. This result can be interpreted as an attempt to understand exactly what makes nonconvex quadratic programming problems hard. Sahni in 1974  showed that quadratic programming with a negative definite quadratic term (
n negative eigenvalues) is NP-hard, whereas Kozlov, Tarasov and Hačijan  showed in 1979 that the ellipsoid algorithm solves the convex quadratic problem (no negative eigenvalues) in polynomial time. This report shows that even one negative eigenvalue makes the problem NP-hard. Key words Global optimization quadratic programming NP-hard
This author's work supported by the Applied Mathematical Sciences Program (KC-04-02) of the Office of Energy Research of the U.S. Department of Energy under grant DE-FG02-86ER25013. A000 and in part by the National Science Foundation, the Air Force Office of Scientific Research, and the Office of Naval Research, through NSF grant DMS 8920550.
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© Kluwer Academic Publishers 1991