We introduce in this paper a technique for the reduced order approximation of
parametric symmetric elliptic partial differential equations. For any given
dimension, we prove the existence of an optimal subspace of at most that
dimension which realizes the best approximation in mean of the error with
respect to the parameter in the quadratic norm associated to the elliptic
operator, between the exact solution and the Galerkin solution calculated on
the subspace. This is analogous to the best approximation property of the
Proper Orthogonal Decomposition (POD) subspaces, excepting that in our case the
norm is parameter-depending, and then the POD optimal sub-spaces cannot be
characterized by means of a spectral problem. We apply a deflation technique to
build a series of approximating solutions on finite-dimensional optimal
subspaces, directly in the on-line step. We prove that the partial sums
converge to the continuous solutions, in mean quadratic elliptic norm.Comment: 18 page
