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Preprocessing for quadratic programming

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Abstract.

Techniques for the preprocessing of (not-necessarily convex) quadratic programs are discussed. Most of the procedures extend known ones from the linear to quadratic cases, but a few new preprocessing techniques are introduced. The implementation aspects are also discussed. Numerical results are finally presented to indicate the potential of the resulting code, both for linear and quadratic problems. The impact of insisting that bounds of the variables in the reduced problem be as tight as possible rather than allowing some slack in these bounds is also shown to be numerically significant.

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References

  1. Andersen, E.D., Andersen, K.D.: Presolving in linear programming. Math. Program. 71(2), 221–245 (1995)

    Article  Google Scholar 

  2. Bongartz, I., Conn, A.R., Gould, N.I.M., Toint, Ph.L.: CUTE: Constrained and Unconstrained Testing Environment. Transactions of the ACM on Math. Softw. 21(1), 123–160 (1995)

    Article  MATH  Google Scholar 

  3. Bradley, G.H., Brown, G.G., Graves, G.W.: Structural redundancy in large-scale optimization models. In: M.H. Karwan et al., (ed.), Redundancy in Mathematical Programming, Springer Verlag, Heidelberg, 1983, pp. 145–169

  4. Brearley, A.L., Mitra, G., Williams, H.P.: Analysis of mathematical programming problems prior to applying the simplex algorithm. Math. Program. 8(1), 54–83 (1975)

    MATH  Google Scholar 

  5. Conn, A.R., Gould, N.I.M., Orban, D., Toint, Ph.L.: A primal-dual trust-region algorithm for minimizing a non-convex function subject to bound and linear equality constraints. Math. Program. 87(2), 215–249 (2000)

    Article  Google Scholar 

  6. Duff, I.S., Erisman, A.M., Reid, J.K.: Direct Methods for Sparse Matrices. Oxford University Press, Oxford, England, 1986

  7. Ferris, M.C., Munson, T.S.: Preprocessing complementarity problems. In: M.C. Ferris, O.Mangasarian and J.S. Pang, (eds.), Complementarity: Applications, Algorithms and Extensions, Kluwer Academic Publishers, Dordrecht, 2001 pp. 143–164

  8. Fourer, R., Gay, D.M.: Experience with a primal presolve algorithm. In: W.W. Hager, D.W. Hearn and P.M. Pardalos, (eds.), Large Scale Optimization: State of the Art, Kluwer Academic Publishers, Dordrecht, 1994, pp. 135–154

  9. Gondzio, J.: Presolve analysis of linear programs prior to applying an interior point method. INFORMS J. Comput. 9(1), 73–91 (1997)

    Google Scholar 

  10. Gould, N.I.M., Toint, Ph.L.: Numerical methods for large-scale non-convex quadratic programming. In: A.H. Siddiqi and M.Kočvara, (eds.), Trends in Industrial and Applied Mathematics, Kluwer Academic Publishers, Dordrecht, 2002 pp. 149–179

  11. Gould, N.I.M., Orban, D., Toint, Ph.L.: CUTEr, a contrained and unconstrained testing environment, revisited. Transactions of the ACM on Mathematical Software, to appear (2003a)

  12. Gould, N.I.M., Orban, D., Toint, Ph.L.: GALAHAD – a library of thread-safe Fortran90 packages for large-scale nonlinear optimization. Transactions of the ACM on Mathematical Software, to appear (2003b)

  13. Gould, N.I.M., Orban, D., Sartenaer, A., Toint, Ph.L.: Componentwise fast convergence in solving nonlinear equations. Math. Program., Ser. B 92(3), 481–508 (2002)

    Google Scholar 

  14. Ioslovich, I.: Robust reduction of a class of large-scale linear programs. SIAM J. Optim. 12(1), 262–282 (2001)

    Article  MATH  Google Scholar 

  15. Tomlin, J.A., Welch, J.S.: Formal optimization of some reduced linear programming problems. Math. Program. 27(2), 232–240 (1983a)

    MATH  Google Scholar 

  16. Tomlin, J.A., Welch, J.S.: A pathological case in the reduction of linear programs. Oper. Res. Lett. 2, 53–57 (1983b)

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Correspondence to Nick Gould.

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Mathamatics Subject Classification (2000): 20E28, 20G40, 20C20

To Roger Fletcher, friend, mentor and inspiration, for his 65th birthday

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Gould, N., Toint, P. Preprocessing for quadratic programming. Math. Program., Ser. B 100, 95–132 (2004). https://doi.org/10.1007/s10107-003-0487-2

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