A new method, the sequential subspace method (SSM), is developed for the pr
oblem of minimizing a quadratic over a sphere. In our scheme, the quadratic
is minimized over a subspace which is adjusted in successive iterations to
ensure convergence to an optimum. When a sequential quadratic programming
iterate is included in the subspace, convergence is locally quadratic. Nume
rical comparisons with other recent methods are given.