Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem

TL;DR

This work extends generalized scaling to a positive vector of parameters, employing a positive vector of parameters, which allows much more flexibility and thus potentially reduces the gaps further, and gives mathematical results aimed at supporting algorithmic methods for computing optimal generalized scalings.

Abstract

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, working with a variety of concave continuous relaxations of the objective function. A standard and computationally-important bound-enhancement technique in this context is (ordinary) scaling, via a single positive parameter. Scaling adjusts the shape of continuous relaxations to reduce the gaps between the upper bounds and the optimal value. We extend this technique to generalized scaling, employing a positive vector of parameters, which allows much more flexibility and thus potentially reduces the gaps further. We give mathematical results aimed at supporting algorithmic methods for computing optimal generalized scalings, and we give computational results demonstrating the performance of generalized scaling on benchmark problem instances.

Theorems & Definitions (3)

• Theorem 1
• remark 1
• proof : Theorem \ref{['thm:bqp']}