Skip to main content
Log in

Approximation algorithms for indefinite quadratic programming

  • Published:
Mathematical Programming Submit manuscript

Abstract

We considerε-approximation schemes for indefinite quadratic programming. We argue that such an approximation can be found in polynomial time for fixedε andt, wheret denotes the number of negative eigenvalues of the quadratic term. Our algorithm is polynomial in 1/ε for fixedt, and exponential int for fixedε.

We next look at the special case of knapsack problems, showing that a more efficient (polynomial int) approximation algorithm exists.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • R.E. Bellman,Dynamic Programming (Princeton University Press, Princeton, NJ, 1957).

    Google Scholar 

  • P. Brucker, “An O(n) algorithm for quadratic knapsack problems,”Operations Research Letters 3 (1984) 163–166.

    Google Scholar 

  • R.W. Cottle, S.G. Duvall and K. Zikan, “A Lagrangean relaxation algorithm for the constrained matrix problem,”Naval Research Logistics Quarterly 33 (1986) 55–76.

    Google Scholar 

  • R.M. Freund, R. Roundy, and M.J. Todd, “Identifying the set of always active constraints in a system of linear inequalities by a single linear program,” Working Paper 1674-85, Sloan School of Management, MIT (Cambridge, MA, 1985).

    Google Scholar 

  • P.E. Gill, W. Murray and M.H. Wright,Practical Optimization (Academic Press, London, 1981).

    Google Scholar 

  • G.H. Golub and C.F. Van Loan,Matrix Computations (Johns Hopkins University Press, Baltimore, MD, 1989, 2nd ed.).

    Google Scholar 

  • R. Helgason, J. Kennington and H. Lall, “A polynomially bounded algorithm for a singly constrained quadratic program,”Mathematical Programming 18 (1980) 338–343.

    Google Scholar 

  • J. Hershberger, “Finding the upper envelope ofn line segments in O(n logn) time,”Information Processing Letters 33 (1989) 169–174.

    Google Scholar 

  • S. Kapoor and P.M. Vaidya, “Fast algorithms for convex quadratic programming and multicommodity flows,” in:Proceedings of the 18th Annual ACM Symposium on Theory of Computing (ACM Press, New York, 1986) pp. 147–159.

    Google Scholar 

  • M.K. Kozlov, S.P. Tarasov and L.G. Hačijan, “Polynomial solvability of convex quadratic programming,”Doklad Akademii Nauk SSSR 248 (1979) 1049–1051. [Translated in:Soviet Mathematics Doklady 20 (1979) 1108–1111.]

    Google Scholar 

  • L. Lovász,An Algorithmic Theory of Numbers, Graphs and Convexity (SIAM, Philadelphia, PA, 1986).

    Google Scholar 

  • J.J. Moré and S.A. Vavasis, “On the solution of concave knapsack problems,”Mathematical Programming 49 (1991) 397–411.

    Google Scholar 

  • K.G. Murty and S.N. Kabadi, “Some NP-complete problems in quadratic and nonlinear programming,”Mathematical Programming 39 (1987) 117–129.

    Google Scholar 

  • A.S. Nemirovsky and D.B. Yudin,Problem Complexity and Method Efficiency in Optimization (Wiley, Chichester, 1983). [Translated by E.R. Dawson fromSlozhnost' Zadach i Effektivnost' Metodov Optimizatsii (1979).]

    Google Scholar 

  • C.H. Papadimitriou and K. Steiglitz,Combinatorial Optimization:Algorithms and Complexity (Prentice-Hall, Englewood Cliffs, NJ, 1982).

    Google Scholar 

  • P.M. Pardalos and J.B. Rosen,Constrained Global Optimization: Algorithms and Applications, Lecture Notes in Computer Science No. 268 (Springer, Berlin, 1987).

    Google Scholar 

  • P.M. Pardalos and S.A. Vavasis, “Quadratic programming with one negative eigenvalue is NP-hard,”Journal of Global Optimization 1 (1990) 15–22.

    Google Scholar 

  • S. Sahni, “Computationally related problems,”SIAM Journal on Computing 3 (1974) 262–279.

    Google Scholar 

  • P.M. Vaidya, “Speeding-up linear programming using fast matrix multiplication (extended abstract),”Proceedings of the 30th Symposium on Foundations of Computer Science (ACM Press, New York, 1989) pp. 332–337.

    Google Scholar 

  • S.A. Vavasis, “Quadratic programming is in NP,”Information Processing Letters 36 (1990) 73–77.

    Google Scholar 

  • S.A. Vavasis, “Approximation algorithms for indefinite quadratic programming,” Technical Report 91-1228, Department of Computer Science, Cornell University (Ithaca, NY, 1991).

    Google Scholar 

  • S.A. Vavasis, “On approximation algorithms for concave quadratic programming,” in: C.A. Floudas and P.M. Pardalos, eds.,Recent Advances in Global Optimization (Princeton University Press, Princeton, NJ, 1992a) pp. 3–18.

    Google Scholar 

  • S.A. Vavasis, “Local minima for indefinite quadratic knapsack problems,”Mathematical Programming 54 (1992b) 127–153.

    Google Scholar 

  • Y. Ye and E. Tse, “An extension of Karmarkar's projective algorithm for convex quadratic programming,”Mathematical Programming 44 (1989) 157–179.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Part of this work was done while the author was visiting Sandia National Laboratories, Albuquerque, New Mexico, supported by the U.S. Department of Energy under contract DE-AC04-76DP00789. Part of this work was also 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.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Vavasis, S.A. Approximation algorithms for indefinite quadratic programming. Mathematical Programming 57, 279–311 (1992). https://doi.org/10.1007/BF01581085

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01581085

Keywords

Navigation