On The Need for Special Purpose Algorithms for Minimax Eigenvalue Problems.

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dc.contributor.authorPanier, E.en_US
dc.contributor.departmentISRen_US
dc.date.accessioned2007-05-23T09:41:30Z
dc.date.available2007-05-23T09:41:30Z
dc.date.issued1988en_US
dc.description.abstractIt has been recently reported that minimax eigenvalue problems can be formulated as nonlinear optimization problems involving smooth objective and constraint functions. This result seems very appealing since minimax eigenvalue problems are known to be typically nondifferentiable. In this paper, we show however that general purpose nonlinear optimization algorithms usually fail to find a solution to these smooth problems even in the simple case of minimization of the maximum eigenvalue of an affine family of symmetric matrices, a convex problem for which efficient algorithms are available.en_US
dc.format.extent373592 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/1903/4781
dc.language.isoen_USen_US
dc.relation.ispartofseriesISR; TR 1988-52en_US
dc.titleOn The Need for Special Purpose Algorithms for Minimax Eigenvalue Problems.en_US
dc.typeTechnical Reporten_US

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