A semidefinite programming-based heuristic for graph coloring
Discrete Applied Mathematics
Algorithms and theory of computation handbook
Computational Optimization and Applications
A Newton-CG Augmented Lagrangian Method for Semidefinite Programming
SIAM Journal on Optimization
ACM Transactions on Mathematical Software (TOMS)
Computational Optimization and Applications
Lower bounds on the global minimum of a polynomial
Computational Optimization and Applications
Hi-index | 0.00 |
We investigate the augmented Lagrangian penalty function approach to solve semidefinite programs. It turns out that this method generates iterates which lie on the boundary of the cone of semidefinite matrices which are driven to the affine subspace described by the linear equations defining the semidefinite program. We provide some computational experience with this method and show in particular, that it allows to compute the theta number of a graph to reasonably high accuracy for instances which are beyond reach by other methods.