# On greedy heuristics for computing D-efficient saturated subsets

## TL;DR

This paper introduces two new greedy algorithms for selecting saturated subsets of vectors that maximize the volume of the parallelotope, improving $D$-optimal experimental design and ellipsoid algorithms.

## Contribution

The paper proposes two novel greedy heuristics for constructing $D$-optimal saturated subsets, with geometric insights and comparisons to existing methods.

## Key findings

- The heuristics outperform random and regularized greedy methods.
- The methods are effective for large sets and alternative criteria.
- Analytic and geometric analysis supports the heuristics' effectiveness.

## Abstract

Let $\mathcal{F}$ be a set consisting of $n$ real vectors of dimension $m \leq n$. For any saturated, i.e., $m$-element, subset $\mathcal{S}$ of $\mathcal{F}$, let $\mathrm{vol}(\mathcal{S})$ be the volume of the parallelotope formed by the vectors of $\mathcal{S}$. A set $\mathcal{S}^*$ is called a $D$-optimal saturated subset of $\mathcal{F}$, if it maximizes $\mathrm{vol}(\mathcal{S})$ among all saturated subsets of $\mathcal{F}$. In this paper, we propose two greedy heuristics for the construction of saturated subsets performing well with respect to the criterion of $D$-optimality: an improvement of the method suggested by Galil and Kiefer for the initiation of $D$-optimal experimental design algorithms, and a modification of the Kumar-Yildirim method, the original version of which was proposed for the initiation of the minimum-volume enclosing ellipsoid algorithms. We provide geometric and analytic insights into the two methods, and compare them to the commonly used random and regularized greedy heuristics. We also suggest variants of the greedy methods for a large set $\mathcal{F}$, for the construction of $D$-efficient non-saturated subsets, and for alternative optimality criteria.