Skip to main content
Log in

An efficient algorithm for sparse null space basis problem using ABS methods

  • Original Paper
  • Published:
Numerical Algorithms Aims and scope Submit manuscript

Abstract

We propose a way to use the Markowitz pivot selection criterion for choosing the parameters of the extended ABS class of algorithms to present an effective algorithm for generating sparse null space bases. We explain in detail an efficient implementation of the algorithm, making use of the special MATLAB 7.0 functions for sparse matrix operations and the inherent efficiency of the ANSI C programming language. We then compare our proposed algorithm with an implementation of an efficient algorithm proposed by Coleman and Pothen with respect to the computing time and the accuracy and the sparsity of the generated null space bases. Our extensive numerical results, using coefficient matrices of linear programming problems from the NETLIB set of test problems show the competitiveness of our implemented algorithm.

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

  1. Abaffy, J., Broyden, C.G., Spedicato, E.: A class of direct methods for linear equations. Numer. Math. 45, 361–376 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  2. Abaffy, J., Broyden, C.G., Spedicato, E.: ABS Projection Algorithms: Mathematical Techniques for Linear and Nonlinear Equations. Ellis Horwood, Chichester (1989)

    MATH  Google Scholar 

  3. Berry, M.W., Heath, M.T., Kaneko, I., Lawo, M., Plemmons, R.J.: An algorithm to compute a sparse basis of the null space. Numer. Math. 47, 483–504 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  4. Le Borne, S.: Block computation and representation of a sparse nullspace basis of a rectangular matrix. Linear Algebra Appl. 428, 2455–2467 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, Z., Deng, N.Y., Xue, Y.: A general algorithm for underdetermined linear systems. In: The Proceedings of the First International Conference on ABS Algorithms, pp. 1–13 (1992)

  6. Coleman, T.S., More, J.J.: Estimation of sparse Jacobian matrices and graph coloring problem. SIAM J. Numer. Anal. 20, 187–209 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  7. Coleman, T.S., More, J.J.: Estimation of sparse Hessian matrices and graph coloring problem. Math. Program. 28, 243–270 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  8. Coleman, T.F., Pothen, A.: The null space problem I. complexity. SIAM J. Algebr. Discrete Methods 7(4), 527–537 (1986)

    Article  MathSciNet  MATH  Google Scholar 

  9. Coleman, T.F., Pothen, A.: The null space problem II. algorithms. SIAM J. Algebr. Discrete Methods 8, 544–563 (1987)

    Article  MathSciNet  Google Scholar 

  10. Davis, T.A.: Algorithm 832: UMFPACK, an unsymmetric-pattern multifrontal method. ACM Trans. Math. Softw. 30(2), 196–199 (2004)

    Article  MATH  Google Scholar 

  11. Davis, T.A.: Direct Methods for Sparse Linear Systems. SIAM, Philadelphia (2006)

    Book  MATH  Google Scholar 

  12. Gay, D.M.: Electronic mail distribution of linear programming test problems. Mathematical Programming Society COAL Newsleter 13, 10–2 (1985)

    Google Scholar 

  13. Gilbert, J.R., Heath, M.T.: Computing a sparse basis for the null space. SIAM J. Algebr. Discrete Methods 8(3), 446–459 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  14. Gilbert, R., Ng, E.G., Peyton, B.W.: Separators and structure prediction in sparse orthogonal factorization. Linear Algebra Appl. 262, 83–97 (1997)

    MathSciNet  MATH  Google Scholar 

  15. Gill, P.E., Murray, M., Wright, M.H.: Practical Optimization. Academic Press, New york (1981)

    MATH  Google Scholar 

  16. Gill, P.E., Murray, W., Saunders, M.A., Wright, M.H.: Maintaining LU factors of a general sparse matrix. Linear Algebra Appl. 88/89, 239–270 (1987)

    Article  MathSciNet  Google Scholar 

  17. Hall, C.: Numerical solution of Navier–Stokes problems by the dual variable method. SIAM J. Algebr. Discrete Methods 6, 220–236 (1985)

    Article  MATH  Google Scholar 

  18. Heath, M.T., Plemmons, R.J., Ward, R.C.: Sparse orthogonal schemes for structural optimization using the force method. SIAM J. Sci. Statist. Comput. 5, 514–532 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  19. Kim, K., Nazareth, J.L.: A primal null-space affine-scaling method. ACM Trans. Math. Softw. 20, 373–392 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  20. Kaneko, I., Lawo, M., Thierauf, G.: On computational procedures for the force method. Int. J. Numer. Methods Eng. 19, 1469–1495 (1982)

    Article  MathSciNet  Google Scholar 

  21. Lu, S.M., Barlow, J.L.: Multifrontal computation with the orthogonal factors of sparse matrices. SIAM J. Matrix Anal. Appl. 17, 658–679 (2006)

    Article  MathSciNet  Google Scholar 

  22. Markowitz, H.M.: The elimination form of the inverse and its application to linear programming. Manage. Sci. 3, 255–269 (1957)

    Article  MathSciNet  MATH  Google Scholar 

  23. Matstoms, P.: Sparse QR factorization in MATLAB. ACM Trans. Math. Softw. 20, 136–159 (1994)

    Article  MATH  Google Scholar 

  24. Plemmons, R., White, R.: Substructuring methods for computing the nullspace of equilibrium matrices. SIAM J. Matrix Anal. Appl. 11, 1–22 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  25. Reid, J.K.: A sparsity exploiting variant of Bartels–Golub decomposition for linear programming bases. Math. Program. 24, 55–69 (1982)

    Article  MATH  Google Scholar 

  26. Spedicato, E., Bodon, E., Xia, E., Mahdavi-Amiri, N.: ABS method for continuous and integer linear equations and optimization. Cent. Eur. J. Oper. Res 18(1), 73–95 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  27. Spedicato, E., Bodon, E., Popolo, A., Mahdavi-Amiri, N.: ABS methods and ABSPACK for linear systems and optimization: a review. 4OR 1(1), 51–66 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  28. Stern, J.M., Vavasis, S.A.: Nested dissection for sparse nullspace bases. SIAM J. Matrix Anal. Appl. 14, 766–775 (1993)

    Article  MathSciNet  MATH  Google Scholar 

  29. Topcu, A.: A contribution to the systematic analysis of finite element structures using the force method. Doctoral Dissertation, University of Essen, Essen, Federal Republic of Germany (1979, in German)

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to N. Mahdavi-Amiri.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Khorramizadeh, M., Mahdavi-Amiri, N. An efficient algorithm for sparse null space basis problem using ABS methods. Numer Algor 62, 469–485 (2013). https://doi.org/10.1007/s11075-012-9599-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11075-012-9599-1

Keywords

Navigation