Minimax and minimax projection designs using clustering

TL;DR

This paper introduces a new hybrid algorithm combining particle swarm optimization and clustering to efficiently generate minimax designs in high-dimensional spaces, and also proposes a novel minimax projection design with improved performance.

Contribution

The paper presents a scalable hybrid algorithm for minimax design generation and introduces minimax projection designs with superior performance on subspaces.

Findings

Algorithm scales linearly with dimension p

Improved minimax performance over existing methods

Proposed minimax projection designs outperform existing designs on subspaces

Abstract

Minimax designs provide a uniform coverage of a design space $\mathcal{X} \subseteq \mathbb{R}^p$ by minimizing the maximum distance from any point in this space to its nearest design point. Although minimax designs have many useful applications, e.g., for optimal sensor allocation or as space-filling designs for computer experiments, there has been little work in developing algorithms for generating these designs, due to its computational complexity. In this paper, a new hybrid algorithm combining particle swarm optimization and clustering is proposed for generating minimax designs on any convex and bounded design space. The computation time of this algorithm scales linearly in dimension $p$, meaning our method can generate minimax designs efficiently for high-dimensional regions. Simulation studies and a real-world example show that the proposed algorithm provides improved minimax performance over existing methods on a variety of design spaces. Finally, we introduce a new type of experimental design called a minimax projection design, and show that this proposed design provides better minimax performance on projected subspaces of $\mathcal{X}$ compared to existing designs. An efficient implementation of these algorithms can be found in the R package minimaxdesign.