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Main Authors: Mukerji, Tantrik, Yang, Wei-Shih
Format: Preprint
Published: 2022
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Online Access:https://arxiv.org/abs/2212.10672
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author Mukerji, Tantrik
Yang, Wei-Shih
author_facet Mukerji, Tantrik
Yang, Wei-Shih
contents We present a randomized algorithm for estimating the permanent of an $M \times M$ real matrix $A$ up to an additive error. We do this by viewing the permanent $\mathrm{perm}(A)$ of $A$ as the expectation of a product of centered joint Gaussian random variables with a particular covariance matrix $C$. The algorithm outputs the empirical mean $S_{N}$ of this product after sampling $N$ times. Our algorithm runs in total time $O(M^{3} + M^{2}N + MN)$ with failure probability \begin{equation*} P(|S_{N}-\text{perm}(A)| > t) \leq \frac{3^{M}}{t^{2}N} \prod^{2M}_{i=1} C_{ii}. \end{equation*} In particular, we can estimate $\mathrm{perm}(A)$ to an additive error of $ε\bigg(\sqrt{3^{2M}\prod^{2M}_{i=1} C_{ii}}\bigg)$ in polynomial time. We compare to a previous procedure due to Gurvits. We discuss how to find a particular $C$ using a semidefinite program and a relation to the Max-Cut problem and cut-norms.
format Preprint
id arxiv_https___arxiv_org_abs_2212_10672
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Additive estimates of the permanent using Gaussian fields
Mukerji, Tantrik
Yang, Wei-Shih
Probability
Data Structures and Algorithms
Combinatorics
Quantum Physics
15A15, 60G15, 68W20
We present a randomized algorithm for estimating the permanent of an $M \times M$ real matrix $A$ up to an additive error. We do this by viewing the permanent $\mathrm{perm}(A)$ of $A$ as the expectation of a product of centered joint Gaussian random variables with a particular covariance matrix $C$. The algorithm outputs the empirical mean $S_{N}$ of this product after sampling $N$ times. Our algorithm runs in total time $O(M^{3} + M^{2}N + MN)$ with failure probability \begin{equation*} P(|S_{N}-\text{perm}(A)| > t) \leq \frac{3^{M}}{t^{2}N} \prod^{2M}_{i=1} C_{ii}. \end{equation*} In particular, we can estimate $\mathrm{perm}(A)$ to an additive error of $ε\bigg(\sqrt{3^{2M}\prod^{2M}_{i=1} C_{ii}}\bigg)$ in polynomial time. We compare to a previous procedure due to Gurvits. We discuss how to find a particular $C$ using a semidefinite program and a relation to the Max-Cut problem and cut-norms.
title Additive estimates of the permanent using Gaussian fields
topic Probability
Data Structures and Algorithms
Combinatorics
Quantum Physics
15A15, 60G15, 68W20
url https://arxiv.org/abs/2212.10672