# Efficient simulation of high dimensional Gaussian vectors

## TL;DR

This paper introduces a linear-storage Markov chain Monte Carlo method for efficiently simulating high-dimensional Gaussian vectors, significantly reducing computational costs compared to traditional Cholesky-based methods.

## Contribution

The authors develop a novel MCMC algorithm that approximates Gaussian vectors with linear storage and provide theoretical bounds on its accuracy and efficiency.

## Key findings

- Linear storage cost in dimension d
- Faster than standard methods by nearly a factor of d
- Provides bounds on Wasserstein distance

## Abstract

We describe a Markov chain Monte Carlo method to approximately simulate a centered d-dimensional Gaussian vector X with given covariance matrix. The standard Monte Carlo method is based on the Cholesky decomposition, which takes cubic time and has quadratic storage cost in d. In contrast, the storage cost of our algorithm is linear in d. We give a bound on the quadractic Wasserstein distance between the distribution of our sample and the target distribution. Our method can be used to estimate the expectation of h(X), where h is a real-valued function of d variables. Under certain conditions, we show that the mean square error of our method is inversely proportional to its running time. We also prove that, under suitable conditions, our method is faster than the standard Monte Carlo method by a factor nearly proportional to d. A numerical example is given.