  The linear complementarity problem is a continuous optimization problem that
generalizes convex quadratic programming, Nash equilibria of bimatrix games and
several such problems. This paper presents a continuous optimization
formulation for the weighted independence number of a graph by characterizing
it as the maximum weighted $\ell_1$ norm over the solution set of a linear
complementarity problem (LCP). The minimum $\ell_1$ norm of solutions of this
LCP is a lower bound on the independent domination number of the graph. Unlike
the case of the maximum $\ell_1$ norm, this lower bound is in general weak, but
we show it to be tight if the graph is a forest. Using methods from the theory
of LCPs, we obtain a few graph theoretic results. In particular, we provide a
stronger variant of the Lov\'{a}sz theta of a graph. We then provide sufficient
conditions for a graph to be well-covered, i.e., for all maximal independent
sets to also be maximum. This condition is also shown to be necessary for
well-coveredness if the graph is a forest. Finally, the reduction of the
maximum independent set problem to a linear program with (linear)
complementarity constraints (LPCC) shows that LPCCs are hard to approximate.
