# Parallel cross interpolation for high-precision calculation of high-dimensional integrals

## TL;DR

This paper introduces a parallel cross interpolation algorithm for high-dimensional integrals, demonstrating superior convergence and scalability over traditional Monte Carlo methods, with applications in quantum physics and statistical models.

## Contribution

The paper presents a novel parallel cross interpolation method that adaptively samples functions for high-dimensional integration, outperforming Monte Carlo approaches in convergence and efficiency.

## Key findings

- Strong superlinear convergence observed
- Exponential convergence with multiple precision arithmetic
- High scalability up to hundreds of processes

## Abstract

We propose a parallel version of the cross interpolation algorithm and apply it to calculate high-dimensional integrals motivated by Ising model in quantum physics. In contrast to mainstream approaches, such as Monte Carlo and quasi Monte Carlo, the samples calculated by our algorithm are neither random nor form a regular lattice. Instead we calculate the given function along individual dimensions (modes) and use this data to reconstruct its behaviour in the whole domain. The positions of the calculated univariate fibers are chosen adaptively for the given function. The required evaluations can be executed in parallel both along each mode (variable) and over all modes. To demonstrate the efficiency of the proposed method, we apply it to compute high-dimensional Ising susceptibility integrals, arising from asymptotic expansions for the spontaneous magnetisation in two-dimensional Ising model of ferromagnetism. We observe strong superlinear convergence of the proposed method, while the MC and qMC algorithms converge sublinearly. Using multiple precision arithmetic, we also observed exponential convergence of the proposed algorithm. Combining high-order convergence, almost perfect scalability up to hundreds of processes, and the same flexibility as MC and qMC, the proposed algorithm can be a new method of choice for problems involving high-dimensional integration, e.g. in statistics, probability, and quantum physics.

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Source: https://tomesphere.com/paper/1903.11554