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Implementing Cholesky factorization for interior point methods of linear programming. (English) Zbl 0819.65097
Summary: Every iteration of an interior point method of large scale linear programming requires computing at least one orthogonal projection of the objective function gradient onto the null space of a linear operator defined by the problem constraint matrix \(A\). The orthogonal projection itself is in turn dominated by the inversion of the symmetric matrix of form \(A \theta A^ T\), where \(\theta\) is a diagonal weighting matrix.
In this paper several specific issues of implementation of the Cholesky factorization that can be applied for solving such equations are discussed. The code called CHFACT being the result of this work is shown to produce comparably sparse factors as the state-of-the-art implementation of the Cholesky decomposition of A. George and J. W. H. Liu [Computer solution of large sparse positive definite systems. Prentice-Hall Series in Computational Mathematics. Englewood Cliffs, New Jersey: Prentice Hall, Inc. XII. (1981; Zbl 0516.65010)]. It has been used for computing projections in an efficient implementation of a higher order primal-dual interior point method of A. Altman and J. Gondzio [Arch. Control. Sci. 2, No. 1-2, 23-40 (1993; Zbl 0799.90083); Eur. J. Oper. Res. 66, No. 1, 158-160 (1993; Zbl 0775.90285)].
Although primary aim of developing CHFACT was to include it into an LP optimizer, the code may equally well be used to solve general large sparse positive definite systems arising in different applications.

65K05 Numerical mathematical programming methods
90C05 Linear programming
65F05 Direct numerical methods for linear systems and matrix inversion
90C06 Large-scale problems in mathematical programming
65F50 Computational methods for sparse matrices
Full Text: DOI
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