A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds | Zendy

Andrew R. Conn | Zendy; Nicholas I. M. Gould | Zendy; Philippe L. Toint | Zendy

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A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds

Author(s) -

Andrew R. Conn,

Nicholas I. M. Gould,

Philippe L. Toint

Publication year - 1991

Publication title -

siam journal on numerical analysis

Language(s) - English

Resource type - Journals

eISSN - 1095-7170

pISSN - 0036-1429

DOI - 10.1137/0728030

Subject(s) - augmented lagrangian method , mathematics , convergence (economics) , simple (philosophy) , bounded function , mathematical optimization , global optimization , nonlinear programming , minification , zero (linguistics) , nonlinear system , lagrangian , class (philosophy) , computer science , mathematical analysis , philosophy , epistemology , economics , economic growth , linguistics , physics , quantum mechanics , artificial intelligence

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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