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Analysis and implementation of a dual algorithm for constrained optimization
1993
Journal of Optimization Theory and Applications
This paper analyzes a constrained optimization algorithm that combines an unconstrained minimization scheme like the conjugate gradient method, an augmented Lagrangian, and multiplier updates to obtain global quadratic convergence. Some of the issues that we focus on are the treatment of rigid constraints that must be satisfied during the iterations and techniques for balancing the error associated with constraint violation with the error associated with optimality. A preconditioner is
doi:10.1007/bf00940552
fatcat:ci26f6hdyjbwfp3q2gxoz3gnti