Abstract
An Augmented Lagrangian algorithm that uses Gauss-Newton approximations of the Hessian at each inner iteration is introduced and tested using a family of Hard-Spheres problems. The Gauss-Newton model convexifies the quadratic approximations of the Augmented Lagrangian function thus increasing the efficiency of the iterative quadratic solver. The resulting method is considerably more efficient than the corresponding algorithm that uses true Hessians. A comparative study using the well-known package LANCELOT is presented.
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Krejić, N., Martínez, J.M., Mello, M. et al. Validation of an Augmented Lagrangian Algorithm with a Gauss-Newton Hessian Approximation Using a Set of Hard-Spheres Problems. Computational Optimization and Applications 16, 247–263 (2000). https://doi.org/10.1023/A:1008716329104
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DOI: https://doi.org/10.1023/A:1008716329104