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dc.contributor.authorLuk, Franklin T.en_US
dc.contributor.authorPagano, Marcelloen_US
dc.description.abstractIn this paper, we study the problem of quadratic programming with M-matrices. We describe (1) an effective algorithm for the case where the variables are subject to a lower bound constraint, and (2) an analogous algorithm for the case where the variables are subject to lower and upper bounds constraints. We demonstrate the special monotone behavior of the iterate and gradient vectors. The result on the gradient vector is new. It leads us to consider a simple updating procedure which preserves the monotonicity of both vectors. The procedure uses the fact that an M-matrix has a non-negative inverse. Two new algorithms are then constructed by incorporating this updating procedure into the two given algorithms. We give numerical examples which show that the new methods can be more efficient than the original ones.en_US
dc.format.extent1102636 bytes
dc.format.extent691938 bytes
dc.publisherCornell Universityen_US
dc.subjectcomputer scienceen_US
dc.subjecttechnical reporten_US
dc.titleQuadratic Programming with M-Matricesen_US
dc.typetechnical reporten_US

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