In this article, we review methods for the solution of unconstrained optimization problems, where the number of unknowns is large.We first describe the basics of unconstrained optimization, then we consider the iterative methods that are commonly used within largescale optimization. The techniques described here explicitly use information on the objective function and some of its derivatives. Extensions to effective quasi-Newtonmethods for partially separable optimization are detailed.

Methods for large scale unconstrained optimization

FASANO, Giovanni
2010-01-01

Abstract

In this article, we review methods for the solution of unconstrained optimization problems, where the number of unknowns is large.We first describe the basics of unconstrained optimization, then we consider the iterative methods that are commonly used within largescale optimization. The techniques described here explicitly use information on the objective function and some of its derivatives. Extensions to effective quasi-Newtonmethods for partially separable optimization are detailed.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/23512
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