Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Andy Conn; N. I. M. Gould; Philippe L. Toint

Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Andy Conn, N. I. M. Gould, Philippe L. Toint

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Abstract

The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.

Original language	English
Pages (from-to)	545-572
Number of pages	28
Journal	SIAM Journal on Numerical Analysis
Volume	28
Issue number	2
Publication status	Published - 1 Apr 1991

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title = "Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds",

abstract = "The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.",

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AU - Toint, Philippe L.

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N2 - The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.

AB - The global and local convergence properties of a class of augmented Lagrangian methods for solving nonlinear programming problems are considered. In such methods, simple bound constraints are treated separately from more general constraints and the stopping rules for the inner minimization algorithm have this in mind. Global convergence is proved, and it is established that a potentially troublesome penalty parameter is bounded away from zero.

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JO - SIAM Journal on Numerical Analysis

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Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Abstract

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ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

LANCELOT: LANCELOT, a package for the solution of large-scale nonlinear optimization problems

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Globally convergent augmented Lagrangian algorithm for optimization with general constraints and simple bounds

Abstract

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ADALGOPT: ADALGOPT - Advanced algorithms in nonlinear optimization

LANCELOT: LANCELOT, a package for the solution of large-scale nonlinear optimization problems

Cite this