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Preconditioning meets biased compression for efficient distributed optimization

Author

Listed:
  • Vitali Pirau

    (Moscow Institute of Physics and Technology)

  • Aleksandr Beznosikov

    (Moscow Institute of Physics and Technology)

  • Martin Takáč

    (Mohamed bin Zayed University of Artificial Intelligence)

  • Vladislav Matyukhin

    (Moscow Institute of Physics and Technology)

  • Alexander Gasnikov

    (Moscow Institute of Physics and Technology)

Abstract

Methods with preconditioned updates show up well in badly scaled and/or ill-conditioned convex optimization problems. However, theoretical analysis of these methods in distributed setting is not yet provided. We close this issue by studying preconditioned version of the Error Feedback (EF) method, a popular convergence stabilization mechanism for distributed learning with biased compression. We combine EF and EF21 algorithms with preconditioner based on Hutchinson’s approximation to the diagonal of the Hessian. An experimental comparison of the algorithms with the ResNet computer vision model is provided.

Suggested Citation

  • Vitali Pirau & Aleksandr Beznosikov & Martin Takáč & Vladislav Matyukhin & Alexander Gasnikov, 2024. "Preconditioning meets biased compression for efficient distributed optimization," Computational Management Science, Springer, vol. 21(1), pages 1-22, June.
    • Handle: RePEc:spr:comgts:v:21:y:2024:i:1:d:10.1007_s10287-023-00496-6
    DOI: 10.1007/s10287-023-00496-6
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