Optimal experimental design and quadratic optimisation

A well known gradient-type algorithm for solving quadratic opti- mization problems is the method of Steepest Descent. Here the Steepest Descent algorithm is generalized to a broader family of gradient algorithms, where the step-length γk is chosen in accordance with a particular procedure. The asymp- totic rate of convergence of this family is studied. To facilitate the investigation we re-write the algorithms in a normalized form which enables us to exploit a link with the theory of optimum experimental design.