  This paper introduces the proximally stabilized Fischer-Burmeister method
(FBstab); a new algorithm for convex quadratic programming that synergistically
combines the proximal point algorithm with a primal-dual semismooth Newton-type
method. FBstab is numerically robust, easy to warmstart, handles degenerate
primal-dual solutions, detects infeasibility/unboundedness and requires only
that the Hessian matrix be positive semidefinite. We outline the algorithm,
provide convergence and convergence rate proofs, report some numerical results
from model predictive control benchmarks, and also include experimental
results. We show that FBstab is competitive with and often superior to, state
of the art methods, has attractive scaling properties, and is especially
promising for model predictive control applications.
