Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem
Optimization and Control
2023-02-13 v1
Abstract
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.
Cite
@article{arxiv.2302.04934,
title = {Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem},
author = {Zhongzhu Chen and Marcia Fampa and Jon Lee},
journal= {arXiv preprint arXiv:2302.04934},
year = {2023}
}