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Abstract: In this study we develop Augmented Lagrangian methods for several types of optimization problems. 1. General nonlinear smooth constrained optimization. We introduce an Augmented Lagrangian scheme, which utilizes a new, highly efficient, mixed quadraticlogarithmic penalty function, and propose several modifications in the updating scheme of Lagrange multipliers, which stabilize the convergence of the algorithm. 2. Optimization over the cone of positive semidefinite matrices (Semidefinite... (Update)
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BibTeX entry: (Update)
@misc{ zibulevsky-penaltybarrier,
author = "Michael Zibulevsky",
title = "Penalty/Barrier Multiplier Methods for Large-Scale Nonlinear and Semidefinite
Programming",
url = "citeseer.ist.psu.edu/zibulevsky96penaltybarrier.html" }
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