Householder Dice: A Matrix-Free Algorithm for Simulating Dynamics on Gaussian and Random Orthogonal Ensembles

• URL: http://arxiv.org/abs/2101.07464v2
• Date: Fri, 22 Jan 2021 00:15:14 GMT
• Title: Householder Dice: A Matrix-Free Algorithm for Simulating Dynamics on Gaussian and Random Orthogonal Ensembles
• Authors: Yue M. Lu
• Abstract summary: Householder Dice (HD) is an algorithm for simulating dynamics on dense random matrix ensembles with translation-invariant properties. The memory and costs of the HD algorithm are $mathcalO(nT)$ and $mathcalO(nT2)$, respectively. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems.
• Score: 12.005731086591139
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper proposes a new algorithm, named Householder Dice (HD), for simulating dynamics on dense random matrix ensembles with translation-invariant properties. Examples include the Gaussian ensemble, the Haar-distributed random orthogonal ensemble, and their complex-valued counterparts. A "direct" approach to the simulation, where one first generates a dense $n \times n$ matrix from the ensemble, requires at least $\mathcal{O}(n^2)$ resource in space and time. The HD algorithm overcomes this $\mathcal{O}(n^2)$ bottleneck by using the principle of deferred decisions: rather than fixing the entire random matrix in advance, it lets the randomness unfold with the dynamics. At the heart of this matrix-free algorithm is an adaptive and recursive construction of (random) Householder reflectors. These orthogonal transformations exploit the group symmetry of the matrix ensembles, while simultaneously maintaining the statistical correlations induced by the dynamics. The memory and computation costs of the HD algorithm are $\mathcal{O}(nT)$ and $\mathcal{O}(nT^2)$, respectively, with $T$ being the number of iterations. When $T \ll n$, which is nearly always the case in practice, the new algorithm leads to significant reductions in runtime and memory footprint. Numerical results demonstrate the promise of the HD algorithm as a new computational tool in the study of high-dimensional random systems.

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