Skip to main content
Log in

A parallel interior point algorithm for linear programming on a network of transputers

  • Section II Algorithms For Parallel Computers
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Interior Point algorithms have become a very successful tool for solving large-scale linear programming problems. The Dual Affine algorithm is one of the Interior Point algorithms implemented in the computer program OB1. It is a good candidate for implementation on a parallel computer because it is very computing-intensive. A parallel Dual Affine algorithm is presented which is suitable for a parallel computer with a distributed memory. The algorithm obtains its speedup from parallel sparse linear algebra computations such as Cholesky factorisation, matrix multiplication, and triangular system solving, which form the bulk of the computing work. Efficient algorithms based on the grid distribution of matrices are presented for each of these computations. The algorithm is implemented in occam 2 on a square mesh of transputers. The resulting parallel program is connected to the sequentialFortran 77 program OB1, which performs the preprocessing and the postprocessing. Experimental results on a mesh of 400 transputers are given for a test set of seven realistic planning and scheduling problems from Shell and seven problems from the NETLIB LP collection; the results show a speedup of 88 for the largest problem.

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. I. Adler, N. Karmarkar, M.G.C. Resende and G. Veiga, Data structures and programming techniques for the implementation of Karmarkar's algorithm, ORSA J. Comput. 1(1989)84–106.

    Google Scholar 

  2. I. Adler, M.G.C. Resende, G. Veiga and N. Karmarkar, An implementation of Karmarkar's algorithm for linear programming, Math. Progr. 44(1989)297–335.

    Google Scholar 

  3. E. Anderson and Y. Saad, Solving sparse triangular linear systems on parallel computers, Int. J. High Speed Comput. 1(1989)73–95.

    Google Scholar 

  4. C. Ashcraft, S.C. Eisenstat and J.W.H. Liu, A fan-in algorithm for distributed sparse numerical factorization, SIAM J. Sci. Stat. Comput. 11(1990)593–599.

    Google Scholar 

  5. C. Ashcraft, S.C. Eisenstat, J.W.H. Liu and A.H. Sherman, A comparison of three column-based distributed sparse factorization schemes, Technical Report CS-90-09, Dept. of Computer Science, York University, North York, Ontario, Canada (1990).

    Google Scholar 

  6. R.H. Bisseling and J.G.G. van de Vorst, Parallel LU decomposition on a transputer network, in:Lecture Notes in Computer Science 384 (Springer, Berlin, 1989) pp. 61–77.

    Google Scholar 

  7. R.H. Bisseling and J.G.G. van de Vorst, Parallel triangular system solving on a mesh network of transputers, SIAM J. Sci. Stat. Comput. 12(1991)787–799.

    Google Scholar 

  8. I.S. Duff, A.M. Erisman and J.K. Reid,Direct Methods for Sparse Matrices (Oxford University Press, Oxford, UK, 1986).

    Google Scholar 

  9. G.C. Fox, M.A. Johnson, G.A. Lyzenga, S.W. Otto, J.K. Salmon and D.W. Walker,Solving Problems on Concurrent Processors, Vol. 1 (Prentice-Hall, Englewood Cliffs, NJ, 1988).

    Google Scholar 

  10. D.M. Gay, Electronic mail distribution of linear programming test problems, Math. Progr. Soc. COAL Newsl. 13(Dec. 1985)10–12.

    Google Scholar 

  11. A. George, Nested dissection of a regular finite element mesh, SIAM J. Numer. Anal. 10(1973)345–363.

    Google Scholar 

  12. A. George, M.T. Heath, J. Liu and E. Ng, Sparse Cholesky factorization on a local-memory multiprocessor, SIAM J. Sci. Stat. Comput. 9(1988)327–340.

    Google Scholar 

  13. A. George and J.W.H. Liu, The evolution of the minimum degree ordering algorithm, SIAM Rev. 31(1989)1–19.

    Google Scholar 

  14. P.E. Gill, W. Murray, M.A. Saunders and M.H. Wright, A note on nonlinear approaches to linear programming, Technical Report SOL 86-7, Department of Operations Research, Standford University, Stanford, CA (1986).

    Google Scholar 

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

    Google Scholar 

  16. M.T. Heath, E. Ng and B.W. Peyton, Parallel algorithms for sparse linear systems, SIAM Rev. 33(1991)420–460.

    Google Scholar 

  17. R.V. Helgason, J.L. Kennington and H.A. Zaki, A parallelization of the Simplex method, Ann. Oper. Res. 14(1988)17–40.

    Google Scholar 

  18. Inmos Ltd.,occam 2 Reference Manual (Prentice-Hall, Hemel Hempstead, UK, 1988).

    Google Scholar 

  19. S.L. Johnsson, Communication efficient basic linear algebra computations on hypercube architectures, J. Parallel Distr. Comput. 4(1987)133–172.

    Google Scholar 

  20. N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica 4(1984)373–395.

    Google Scholar 

  21. D.E. Knuth,The Art of Computer Programming, Vol. 1, 2nd ed. (Addison-Wesley, Reading, MA, 1973).

    Google Scholar 

  22. M. Kojima, S. Mizuno and A. Yoshise, A Primal-Dual Interior Point algorithm for linear programming, in: N. Megiddo (ed.),Progress in Mathematical Programming, (Springer, New York, 1988) pp. 29–47.

    Google Scholar 

  23. R. Levkovitz and G. Mitra, Solution of large sparse symmetric equations on a transputer network, in:Proc. 3rd Int. Conf. on Applications of Transputers, T.S. Durrani et al. (eds.), (IOS Press, Amsterdam, 1991) pp. 105–110.

    Google Scholar 

  24. R. Levkovitz and G. Mitra, Cholesky factorization of sparse symmetric positive definite matrices on distributed parallel computers, in:Transputing in Numerical and Neural Network Applications, ed. G.L. Reijns and J. Luo (IOS Press, Amsterdam, 1992) pp. 30–47.

    Google Scholar 

  25. J.W.H. Liu, Modification of the minimum-degree algorithm by multiple elimination, ACM Trans. Math. Softw. 11(1985)141–153.

    Google Scholar 

  26. J.W.H. Liu, A compact row storage scheme for Cholesky factors using elimination trees, ACM Trans. Math. Softw. 12(1986)127–148.

    Google Scholar 

  27. J.W.H. Liu, The role of elimination trees in sparse factorization, SIAM J. Matrix Anal. Appl. 11(1990)134–172.

    Google Scholar 

  28. L.D.J.C. Loyens, A design method for parallel programs, Ph.D. Thesis, Dept. of Mathematics and Computing Science, Edindhoven University of Technology, The Netherlands (1992).

    Google Scholar 

  29. L.D.J.C. Loyens and R.H. Bisseling, The formal construction of a parallel triangular system solver, in:Lecture Notes in Computer Science 375 (Springer, Berlin, 1989) pp. 325–334.

    Google Scholar 

  30. I.J. Lustig, R.E. Marsten and D.F. Shanno, Computational experience with a Primal-Dual Interior Point method for linear programming, Lin. Alg. Appl. 152(1991)191–222.

    Google Scholar 

  31. I.J. Lustig, R.E. Marsten and D.F. Shanno, On implementing Mehrotra's predictor-corrector Interior-Point method for linear programming, SIAM J. Optim. 2(1992)435–449.

    Google Scholar 

  32. R.E. Marsten, M.J. Saltzman, D.F. Shanno, G.S. Pierce and J.F. Ballintijn, Implementation of a Dual Affine Interior Point algorithm for linear programming, ORSA J. Comput. 1(1989)287–297.

    Google Scholar 

  33. R. Marsten, R. Subramanian, M. Saltzman, I. Lustig and D. Shanno, Interior Point methods for linear programming: Just call Newton, Lagrange, and Fiacco and McCormick!, Interfaces 20(1990)105–116.

    Google Scholar 

  34. S. Mehrotra, On the implementation of a Primal-Dual Interior Point method, SIAM J. Optim. 2(1992)575–601.

    Google Scholar 

  35. D.P. O'Leary and G.W. Stewart, Data-flow algorithms for parallel matrix computations, Commun. ACM 28(1985)840–853.

    Google Scholar 

  36. R. Schreiber, A new implementation of sparse Gaussian elimination, ACM Trans. Math. Softw. 8(1982)256–276.

    Google Scholar 

  37. A.F. van der Stappen, R.H. Bisseling and J.G.G. van de Vorst, Parallel sparse LU decomposition on a mesh network of transputers, SIAM J. Matrix Anal. Appl. 14(1993)853–879.

    Google Scholar 

  38. C.B. Stunkel and D.A. Reed, Hypercube implementation of the Simplex algorithm, in:Proc. 3rd Conf. on Hypercube Concurrent Computers and Applications, Vol. 2, G. Fox (ed.) ACM Press, New York, 1988) pp. 1473–1482.

    Google Scholar 

  39. J.A. Tomlin, An experimental approach to Karmarkar's projective method for linear programming, Math. Progr. Study 31(1987)175–191.

    Google Scholar 

  40. J.G.G. van de Vorst, The formal development of a parallel program performing LU-decomposition, Acta Inform. 26(1988)1–17.

    Google Scholar 

  41. J.G.G. van de Vorst, Solving the least squares problem using a parallel linear algebra library, Future Generation Comp. Syst. 4(1989)293–297.

    Google Scholar 

  42. J.G.G. van de Vorst, An attempt to use parallel computing in large scale optimisation, in:Logistics: where ends have to meet, C.F.H. van Rijn (ed.) (Pergamon Press, Oxford, UK, 1989) pp. 112–119.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bisseling, R.H., Doup, T.M. & Loyens, L.D.J.C. A parallel interior point algorithm for linear programming on a network of transputers. Ann Oper Res 43, 49–86 (1993). https://doi.org/10.1007/BF02024486

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF02024486

Keywords

Navigation