  We propose an algorithm for solving bound-constrained mathematical programs
with complementarity constraints on the variables. Each iteration of the
algorithm involves solving a linear program with complementarity constraints in
order to obtain an estimate of the active set. The algorithm enforces descent
on the objective function to promote global convergence to B-stationary points.
We provide a convergence analysis and preliminary numerical results on a range
of test problems. We also study the effect of fixing the active constraints in
a bound-constrained quadratic program that can be solved on each iteration in
order to obtain fast convergence.
