On the role of energy convexity in the web function approximation

For a given p > 1 and an open bounded convex set we consider the minimization problem for the functional over Since the energy of the unique minimizer u_p may not be computed explicitly, we restrict the minimization problem to the subspace of web functions, which depend only on the distance from the boundary δΩ. In this case, a representation formula for the unique minimizer v_p is available. Hence the problem of estimating the error one makes when approximating J_p(u_p) by J_p(v_p) arises. When Ω varies among convex bounded sets in the plane, we find an optimal estimate for such error, and we show that it is decreasing and infinitesimal with p. As p → ∞, we also prove that u_p − v_p converges to zero in for all m < ∞. These results reveal that the approximation of minima by means of web functions gains more and more precision as convexity in J_p increases.