In the context of augmented Lagrangian approaches for solving semidefinite programming problems, we investigate the possibility of eliminating the positive semidefinite constraint on the dual matrix by employing a factorization. Hints on how to deal with the resulting unconstrained maximization of the augmented Lagrangian are given. We further use the approximate maximum of the augmented Lagrangian with the aim of improving the convergence rate of alternating direction augmented Lagrangian frameworks. Numerical results are reported, showing the benefits of the approach.
2018, OPERATIONS RESEARCH LETTERS, Pages 523-528 (volume: 46)
Using a factored dual in augmented Lagrangian methods for semidefinite programming (01a Articolo in rivista)
De Santis Marianna, Rendl Franz, Wiegele Angelika
Gruppo di ricerca: Continuous Optimization