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dc.contributor.authorBaryamureeba, Venansius
dc.contributor.authorSteihaug, Trond
dc.contributor.authorZhang, Yin
dc.date.accessioned2022-07-17T15:16:02Z
dc.date.available2022-07-17T15:16:02Z
dc.date.issued1999
dc.identifier.citationBaryamureeba, V., & Steihaug, T. (1999, August). Application of a class of preconditioners to large scale linear programming problems. In European Conference on Parallel Processing (pp. 1044-1048). Springer, Berlin, Heidelberg.en_US
dc.identifier.urihttps://link.springer.com/chapter/10.1007/3-540-48311-X_146
dc.identifier.urihttps://nru.uncst.go.ug/handle/123456789/4210
dc.description.abstractIn most interior point methods for linear programming, a sequence of weighted linear least squares problems are solved, where the only changes from one iteration to the next are the weights and the right hand side. The weighted least squares problems are usually solved as weighted normal equations by the direct method of Cholesky factorization. In this paper, we consider solving the weighted normal equations by a preconditioned conjugate gradient method at every other iteration. We use a class of preconditioners based on a low rank correction to a Cholesky factorization obtained from the previous iteration. Numerical results show that when properly implemented, the approach of combining direct and iterative methods is promisingen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectWeighted linear least squaresen_US
dc.subjectParallel processingen_US
dc.subjectPreconditionersen_US
dc.subjectLinear programmingen_US
dc.subjectPrimal-dual infeasible interior point algorithmsen_US
dc.titleApplication of a Class of Preconditioners to Large Scale Linear Programming Problemsen_US
dc.typeBook chapteren_US


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