Groupe d’études et de recherche en analyse des décisions

G-2015-109

Scalable adaptative cubic regularization methods

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Adaptative cubic regularization (ARC) methods for unconstrained optimization compute steps from linear systems with a shifted Hessian in the spirit of the modified Newton method. In the simplest case, the shift is a multiple of the identity, which is typically identified by trial and error. We propose a scalable implementation of ARC in which we solve a set of shifted systems concurrently by way of an appropriate Krylov solver.

, 13 pages