The paper presents an algorithm that in a finite number of iterations finds an absolute minimizer for the standard quadratic programming problem with no assumptions on convexity properties of the cost function. It is based on the sequential solution of auxiliary linear complementarity problems by an algorithm recently developed by the author. Some results of computational experiments with test problems of dimensions up to 500 are given.