We present robust projective algorithms of the von Neumann type for the linear complementarity problem and for the generalized linear complementarity problem. The methods, an extension of Projections Onto Convex Sets (POCS) are applied to a class of problems consisting of finding the intersection of closed nonconvex sets. We give conditions under which convergence occurs (always in 2 dimensions, and in practice, in higher dimensions) when the matrices are P -matrices (though not necessarily symmetric or positive definite). We provide numerical results with comparisons to Projective Successive Over Relaxation (PSOR).