Complexity of differential and integral equations

Abstract

We are interested in the intrinsic difficulty (or complexity) of computing an approximate solution of the linear operator equation Lu = f. Practical examples of such problems include the cases where L is a known partial differential or integral operator. Problems of the form Lu = f are typically solved under the constraint that only partial information about f is available, such as the values of a finite number of inner products, or the values of f at a finite number of points. It is of interest to determine when algorithms which are in wide use are optimal algorithms, i.e., algorithms which produce an approximation with minimal cost. We are especially interested in determining conditions which are necessary and sufficient for the finite element method (FEM) to be optimal. For the cases of elliptic partial differential equations and of Fredholm integral equations of the second kind, we describe such a condition, in the form of an inequality involving the order of the problem and the degree of the finite element subspace. Suppose this inequality is violated; is the nonoptimality of the FEM inherent in the information used by the FEM, or is it because the FEM uses this information in a nonoptimal manner? The latter is the case; there always exists an algorithm using this information which is optimal. We also discuss the situation in which the information used by the finite element method (which consists of inner products) is not available. Suppose that the only admissible information about f consists of evaluations of f. In the case of the Fredholm problem of the second kind, this information is optimal; moreover, a finite element method in which the inner products are approximated by quadrature rules is an optimal algorithm. However there exist elliptic problems of positive order for which this new information is nonoptimal.