A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds
SIAM Journal on Numerical Analysis1991Vol. 28(2), pp. 545–572
Citations Over TimeTop 10% of 1991 papers
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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