# Generalized Scaling for the Constrained Maximum-Entropy Sampling Problem

arXiv: https://arxiv.org/abs/2302.04934 · HTML: https://mlsanity.com/paper/2302.04934/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2023.

## Built on by (most cited first, 3 of 3)

- [Parameter-Free Non-Ergodic Extragradient Algorithms for Solving Monotone Variational Inequalities](https://mlsanity.com/paper/2604.07662/) (2026)
- [A physics-informed Bayesian optimization method for rapid development of electrical machines](https://mlsanity.com/paper/2503.00420/) (2025)
- [The hyper-scaled NLP bound for maximum-entropy remote sampling](https://mlsanity.com/paper/2601.20970/) (2026)

## Abstract (arXiv)

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.