Smooth Optimization with Approximate Gradient

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dc.creatord'Aspremont, Alexandre
dc.date2005-12-14
dc.date2008-05-16
dc.date.accessioned2026-07-07T09:39:09Z
dc.date.available2026-07-07T09:39:09Z
dc.descriptionWe show that the optimal complexity of Nesterov's smooth first-order optimization algorithm is preserved when the gradient is only computed up to a small, uniformly bounded error. In applications of this method to semidefinite programs, this means in some instances computing only a few leading eigenvalues of the current iterate instead of a full matrix exponential, which significantly reduces the method's computational cost. This also allows sparse problems to be solved efficiently using sparse maximum eigenvalue packages.
dc.descriptionTitled changed from "Smooth Optimization for Sparse Semidefinite Programs". New figures, tests. Final version
dc.identifierhttps://arxiv.org/abs/math/0512344
dc.identifierhttp://arxiv.org/abs/math/0512344
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/161066
dc.subjectOptimization and Control
dc.subject90C25; 90C22; 90C06
dc.titleSmooth Optimization with Approximate Gradient
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