Optimal Design of Multifactor Experiments via Grid Exploration

TL;DR

This paper introduces a simple adaptive grid exploration algorithm for designing efficient multifactor experiments, outperforming existing methods and providing accessible R code for practical implementation.

Contribution

The paper presents a novel adaptive exploration algorithm for optimal multifactor experimental design, applicable to various factor types and accompanied by open-source R code.

Findings

Algorithm significantly outperforms state-of-the-art methods

Effective for discrete, continuous, and mixed factors

Provides accessible tools for researchers

Abstract

For computing efficient approximate designs of multifactor experiments, we propose a simple algorithm based on adaptive exploration of the grid of all combinations of factor levels. We demonstrate that the algorithm significantly outperforms several state-of-the-art competitors for problems with discrete, continuous, as well as mixed factors. Importantly, we provide a free R code that permits direct verification of the numerical results and allows the researchers to easily compute optimal or nearly-optimal experimental designs for their own statistical models.