中文

Computing Smith Forms Modulo $p^2$ of Sparse Matrices Faster Than Matrix Multiplication

符号计算 2026-07-07 v1

摘要

Let pp be a prime and R=Z/p2ZR=\mathbb{Z}/p^2\mathbb{Z} the ring of integers modulo p2p^2. Any ARn×nA\in R^{n\times n} is unimodularly equivalent to its Smith form S=diag(1,,1r0,p,,pr1,0,,0r2)Rn×n, S=diag\bigl(\underbrace{1,\ldots,1}_{r_0}, \underbrace{p,\ldots,p}_{r_1}, \underbrace{0,\ldots,0}_{r_2}\bigr) \in R^{n\times n}, i.e., there exist U,VRn×nU,V\in R^{n\times n} such that UAV=SUAV=S, with detU,detVR\det U,\det V\in R^* (where RR^* is the set of units in RR, elements not equivalent to 0modp0\bmod p). Our goal in this paper is to determine r0,r1,r2r_0,r_1,r_2 quickly when AA is sparse or structured. By ``sparse'' we mean AA is given by a black box such that for any vRn×1v\in R^{n\times 1} we can compute vAvv\mapsto Av with O~(n)\tilde{O}(n) operations in RR, which captures having few nonzero elements or a multiplicative structure (e.g., Hankel or Toeplitz matrices). We present a randomized algorithm which requires an expected number of O~(n31/(ω1)) \tilde{O}\left(n^{3-1/(\omega-1)}\right) operations in RR to compute the Smith form, where ω\omega is the exponent of dense matrix multiplication. Using standard cubic matrix multiplication (ω=3\omega=3) our algorithm thus requires O~(n2.5)\tilde{O}(n^{2.5}) operations in RR, while using the current asymptotically fastest matrix multiplication, with ω<2.371339\omega<2.371339, our algorithm requires O~(n2.270786)\tilde{O}(n^{2.270786}) operations in RR. Our algorithm is probabilistic of the Monte Carlo type, meaning it fails on any invocation with controllably small probability. We employ iterative block-Wiedemann-style matrix techniques and structured preconditioners. To our knowledge, this is the first algorithm to compute the modular Smith Normal Form modulo p2p^2 requiring fewer than O~(nω)\tilde{O}(n^\omega) operations in RR, i.e., faster than any dense algorithm.

引用

@article{arxiv.2607.05800,
  title  = {Computing Smith Forms Modulo $p^2$ of Sparse Matrices Faster Than Matrix Multiplication},
  author = {Mark Giesbrecht},
  journal= {arXiv preprint arXiv:2607.05800},
  year   = {2026}
}