  This paper considers solving convex quadratic programs (QPs) in a real-time
setting using a regularized and smoothed Fischer-Burmeister method (FBRS). The
Fischer-Burmeister function is used to map the optimality conditions of the
quadratic program to a nonlinear system of equations which is solved using
Newton's method. Regularization and smoothing are applied to improve the
practical performance of the algorithm and a merit function is used to
globalize convergence. FBRS is simple to code, easy to warmstart, robust to
early termination, and has attractive theoretical properties, making it
appealing for real-time and embedded applications. Numerical experiments using
several predictive control examples show that the proposed method is
competitive with other state of the art solvers.
