  The best techniques for the constrained maximum-entropy sampling problem, a
discrete-optimization problem arising in the design of experiments, are via a
variety of concave continuous relaxations of the objective function. A standard
bound-enhancement technique in this context is scaling. We extend this
technique to generalized scaling, we give mathematical results aimed at
supporting algorithmic methods for computing optimal generalized scalings, and
we give computational results demonstrating the usefulness of generalized
scaling on benchmark problem instances.
