A Short Note on Batch-efficient Divide-and-Conquer Algorithm for EigenDecomposition

arXiv:2604.27325v1 Announce Type: new Abstract: 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.

Source: arXiv cs.LG | 2026-05-01