EigenDecomposition (ED) is at the heart of many computer vision algorithms and applications. One crucial bottleneck limiting its usage is the expensive computation cost, particularly for a mini-batch of matrices in deep neural networks. Our previous work proposed a dedicated QR-based ED algorithm for batched small matrices (dim<32). This short paper targets the limitation and proposes a batch-efficient Divide-and-Conquer based ED algorithm for larger matrices. The numerical test shows that for a mini-batch of matrices whose dimensions are smaller than 64, our method can be much faster than the Pytorch SVD function.
@article{arxiv.2604.27325,
title = {A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition},
author = {Yue Song},
journal= {arXiv preprint arXiv:2604.27325},
year = {2026}
}