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

Sergey Dolgov, Dmitry Savostyanov

    Research output: Contribution to journalArticlepeer-review


    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 these values to reconstruct its behaviour in the whole domain. The positions of the calculated univariate fibres are chosen adaptively for the given function. The required evaluations can be executed in parallel 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 observe 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.
    Original languageEnglish
    Article number106869
    JournalComputer Physics Communications
    Publication statusPublished - 23 Aug 2019

    Bibliographical note

    © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license


    • Cross interpolation
    • High precision
    • High-dimensional integration
    • Ising integrals
    • Parallel algorithms
    • Tensor train format


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