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Computational Experience of Linear Equation Solvers for Self-Regular Interior-Point Methods  

Seol Tongryeol (LG CNS)
Publication Information
Korean Management Science Review / v.21, no.2, 2004 , pp. 43-60 More about this Journal
Abstract
Every iteration of interior-point methods of large scale optimization requires computing at least one orthogonal projection. In the practice, symmetric variants of the Gaussian elimination such as Cholesky factorization are accepted as the most efficient and sufficiently stable method. In this paper several specific implementation issues of the symmetric factorization that can be applied for solving such equations are discussed. The code called McSML being the result of this work is shown to produce comparably sparse factors as another implementations in the $MATLAB^{***}$ environment. It has been used for computing projections in an efficient implementation of self-regular based interior-point methods, McIPM. Although primary aim of developing McSML was to embed it into an interior-point methods optimizer, the code may equally well be used to solve general large sparse systems arising in different applications.
Keywords
Self-regular Interior-point Methods; Orthogonal Projections; Sparse Systems; Symmetric Factorization;
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Times Cited By KSCI : 2  (Citation Analysis)
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