Abstract
Linear programming in core using a variant of the Bartels-Golub decomposition of the basis matrix will be considered. This variant is particularly well-adapted to sparsity preservation, being capable of revising the factorisation without any fill-in whenever this is possible by permutations alone. In addition strategies for colum pivoting in the simplex method itself will be discussed and in particular it will be shown that the “steepest edge” algorithm is practical. This algorithm has long been known to give good results in respect of number of iterations, but has been thought to be impractical.
Test results on genuine problems with hundreds of rows and thousands of columns will be reported. These tests include comparisons with other methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bartels, R.H. (1971). A stabilization of the simplex method. Num. Math., 16, 414–434.
Beale, E.M.L. (1971). Sparseness in linear programming. In “Large sparse sets of linear equations”. Ed. J.K. Reid, Academic Press.
Dantzig G.B. (1963). Linear programming and extensions. Princeton University Press.
Duff, I.S. and Reid, J.K. (1974). A comparison of sparsity orderings for obtaining a pivotal sequence in Gaussian elimination. J. Inst. Maths. Applics., 14, 281–291.
Forrest, J.J.H. and Tomlin, J.A. (1972). Updating triangular factors of the basis to maintain sparsity in the product form simplex method. Mathematical programming, 2, 263–278.
Gill, P.E. and Murray, W. (1973). A numerically stable form of the simplex algorithm. Linear Alg. Appl., 7, 99–138.
Goldfarb, D. (1975). On the Bartels-Golub decomposition for linear programming bases. To appear.
Goldfarb, D. and Reid, J.K. (1975a). A practical steepest edge simplex algorithm. To appear.
Goldfarb, D. and Reid, J.K. (1975b). Fortran subroutines for sparse in-core linear programming. A.E.R.E. Report to appear.
Gill, P.E. (1974). Recent developments in numerically stable methods for linear programming. Bull. Inst. Maths. Applics. 10, 180–186.
Gustavson, F.G. (1972). Some basic techniques for solving sparse systems of linear equations. In “Sparse matrices and their applications”. Ed. D.J. Rose and R.A. Willoughby, Plenum Press.
Harris, P.M.J. (1973). Pivot selection methods of the Devex LP code. Mathematical programming 5, 1–28.
Householder, A.S. (1964). The theory of matrices in numerical analysis. Blaisdell.
Kuhn, H.W. and Quant, R.E. (1963). An experimental study of the simplex method. Proc. of Symposia in Applied Maths, Vol. XV, Ed. Metropolis et al. A.M.S.
Markowitz, H.M. (1957). The elimination form of the inverse and its applications to linear programming. Management Sci., 3, 255–269.
Reid, J.K. (1973). Sparse linear programming using the Bartels-Golub decomposition. Verbal presentation at VIII International Symposium on Mathematical Programming, Stanford University.
Reid, J.K. (1975). A sparsity-exploiting variant of the Bartels-Golub decomposition for linear programming bases. To appear.
Saunders, M.A. (1972). Large-scale linear programming using the Cholesky factorization. Report STAN-CS-72-252, Stanford University.
Tomlin, J.A. (1972). Pivoting for size and sparsity in linear programming inversion routines. J. Inst. Maths. Applics., 10, 289–295.
Editor information
Rights and permissions
Copyright information
© 1976 Springer-Verlag
About this paper
Cite this paper
Reid, J.K. (1976). Sparse in-core linear programming. In: Watson, G.A. (eds) Numerical Analysis. Lecture Notes in Mathematics, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0080124
Download citation
DOI: https://doi.org/10.1007/BFb0080124
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-07610-0
Online ISBN: 978-3-540-38129-7
eBook Packages: Springer Book Archive