On iterated-subspace minimization methods for nonlinear optimization: Proceedings on Linear and Nonlinear Conjugate Gradient-Related Methods

Andy Conn; Nick Gould; Annick Sartenaer; Philippe Toint

On iterated-subspace minimization methods for nonlinear optimization: Proceedings on Linear and Nonlinear Conjugate Gradient-Related Methods

Andy Conn, Nick Gould, Annick Sartenaer, Philippe Toint

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L. Adams and L. Nazareth, eds.), SIAM, pp. 50-78, 1996.

langue originale	Anglais
Etat de la publication	Publié - 1996

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@misc{87e137ff958b4cfd8448b6fb7778563a,

title = "On iterated-subspace minimization methods for nonlinear optimization: Proceedings on Linear and Nonlinear Conjugate Gradient-Related Methods",

abstract = "We consider a class of Iterated-Subspace Minimization (ISM) methods for solving large-scale unconstrained minimization problems. At each major iteration of such a method, a low-dimensional manifold, the iterated subspace, is constructed and an approximate minimizer of the objective function in this manifold then determined. The iterated subspace is chosen to contain vectors which ensure global convergence of the overall scheme and may also contain vectors which encourage fast asymptotic convergence. We demonstrate that this approach can sometimes be very advantageous and indicate the general performance on a collection of large problems. Moreover, comparisons with a limited memory approach and LANCELOT are made. ",

author = "Andy Conn and Nick Gould and Annick Sartenaer and Philippe Toint",

year = "1996",

language = "English",

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T1 - On iterated-subspace minimization methods for nonlinear optimization

T2 - Proceedings on Linear and Nonlinear Conjugate Gradient-Related Methods

AU - Conn, Andy

AU - Gould, Nick

AU - Sartenaer, Annick

AU - Toint, Philippe

PY - 1996

Y1 - 1996

N2 - We consider a class of Iterated-Subspace Minimization (ISM) methods for solving large-scale unconstrained minimization problems. At each major iteration of such a method, a low-dimensional manifold, the iterated subspace, is constructed and an approximate minimizer of the objective function in this manifold then determined. The iterated subspace is chosen to contain vectors which ensure global convergence of the overall scheme and may also contain vectors which encourage fast asymptotic convergence. We demonstrate that this approach can sometimes be very advantageous and indicate the general performance on a collection of large problems. Moreover, comparisons with a limited memory approach and LANCELOT are made.

AB - We consider a class of Iterated-Subspace Minimization (ISM) methods for solving large-scale unconstrained minimization problems. At each major iteration of such a method, a low-dimensional manifold, the iterated subspace, is constructed and an approximate minimizer of the objective function in this manifold then determined. The iterated subspace is chosen to contain vectors which ensure global convergence of the overall scheme and may also contain vectors which encourage fast asymptotic convergence. We demonstrate that this approach can sometimes be very advantageous and indicate the general performance on a collection of large problems. Moreover, comparisons with a limited memory approach and LANCELOT are made.

M3 - Other contribution

ER -

On iterated-subspace minimization methods for nonlinear optimization: Proceedings on Linear and Nonlinear Conjugate Gradient-Related Methods

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