Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem
Zhongzhu Chen, Marcia Fampa, Jon Lee · Society for Industrial and Applied Mathematics eBooks · 2023
The best practical techniques for exact solution of instances of the constrained maximum-entropy sampling problem, a discrete-optimization problem arising in the design of experiments, are via a branch-and-bound framework applied to a variety of concave continuous relaxations of the objective function. A standard and computationally-important bound-enhancement technique in this context is scaling, via a single positive parameter. We extend this technique to generalized scaling, employing a positive vector of parameters, 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.