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dc.contributor.authorColeman, Thomas F.en_US
dc.date.accessioned2007-04-23T17:14:58Z
dc.date.available2007-04-23T17:14:58Z
dc.date.issued1986-06en_US
dc.identifier.citationhttp://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR86-762en_US
dc.identifier.urihttps://hdl.handle.net/1813/6602
dc.description.abstractWe propose an automatic preconditioning scheme for large sparse numerical optimization. The strategy is based on an examination of the sparsity pattern of the Hessian matrix: using a graph-theoretic heuristic, a block diagonal approximation to the Hessian matrix is induced. The blocks are submatrices of the Hessian matrix; furthermore, each block is chordal. That is, under a positive definiteness assumption, each block can be Cholesky factored without creating new nonzeroes (fill). Therefore the preconditioner is space efficient. We conduct a number of numerical experiments to determine the effectiveness of the preconditioner in the context of a linear conjugate gradient algorithm for optimization.en_US
dc.format.extent2302421 bytes
dc.format.extent695887 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/postscript
dc.language.isoen_USen_US
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleA Chordal Preconditioner for Large Scale Optimizationen_US
dc.typetechnical reporten_US


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