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This paper considers solving convex quadratic programs (QPs) in a real-timesetting using a regularized and smoothed Fischer-Burmeister method (FBRS). TheFischer-Burmeister function is used to map the optimality conditions of thequadratic program to a nonlinear system of equations which is solved usingNewton's method. Regularization and smoothing are applied to improve thepractical performance of the algorithm and a merit function is used toglobalize convergence. FBRS is simple to code, easy to warmstart, robust toearly termination, and has attractive theoretical properties, making itappealing for real-time and embedded applications. Numerical experiments usingseveral predictive control examples show that the proposed method iscompetitive with other state of the art solvers.

A Regularized and Smoothed Fischer-Burmeister Method for Quadratic Programming with Applications to Model Predictive Control