# Experimental Design Using Interlacing Polynomials

arXiv: https://arxiv.org/abs/2410.11390 · HTML: https://mlsanity.com/paper/2410.11390/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 15 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Near-Optimal Discrete Optimization for Experimental Design: A Regret Minimization Approach](https://mlsanity.com/paper/1711.05174/) (2017)
- [Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design](https://mlsanity.com/paper/1802.08318/) (2018)
- [Approximation Algorithms for D-optimal Design](https://mlsanity.com/paper/1802.08372/) (2018)
- [On largest volume simplices and sub-determinants](https://mlsanity.com/paper/1406.3512/) (2014)
- [A Spectral Approach to Network Design](https://mlsanity.com/paper/2003.07810/) (2020)
- [A Local Search Framework for Experimental Design](https://mlsanity.com/paper/2010.15805/) (2020)
- [Experimental Design for Any p-Norm](https://mlsanity.com/paper/2305.01942/) (2023)

## Abstract (arXiv)

We present a unified deterministic approach for experimental design problems using the method of interlacing polynomials. Our framework recovers the best-known approximation guarantees for the well-studied D/A/E-design problems with simple analysis. Furthermore, we obtain improved non-trivial approximation guarantee for E-design in the challenging small budget regime. Additionally, our approach provides an optimal approximation guarantee for a generalized ratio objective that generalizes both D-design and A-design.