A globally convergent sequential quadratic programming algorithm for mathematical programs with linear complementarity constraints

This paper presents a sequential quadratic programming algorithm for computing a stationary point of a mathematical program with linear complementarity constraints. The algorithm is based on a reformulation of the complementarity condition as a system of semismooth equations by means of Fischer–Burmeister functional, combined with a classical penalty function method for sloving constrained optimization problems. Global convergence of the algorithm is established under appropriate assumptions. Some preliminary computational results are reported.