A Vectorial Kernel Orthogonal Greedy Algorithm

This work shows that the approximation gain is bounded globally and for the multivariate case the limit functions correspond to a directional Hermite interpolation and proves algebraic convergence similar to [14], improved by a dimension-dependent factor, and introduces a new a-posteriori error bound.