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arXiv · 2401.06332

Distributed Solvers for Network Linear Equations with Scalarized Compression

Lei Wang·Zihao Ren·Deming Yuan·Guodong Shi
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Abstract

Distributed computing is fundamental to multi-agent systems, with solving distributed linear equations as a typical example. In this paper, we study distributed solvers for network linear equations over a network with node-to-node communication messages compressed as scalar values. Our key idea lies in a dimension compression scheme that includes a dimension-compressing vector and a data unfolding step. The compression vector applies to individual node states as an inner product to generate a real-valued message for node communication. In the unfolding step, such scalar message is then plotted along the subspace generated by the compression vector for the local computations. We first present a compressed consensus flow that relies only on such scalarized communication, and show that linear convergence can be achieved with well excited signals for the compression vector. We then employ such a compressed consensus flow as a fundamental consensus subroutine to develop distributed continuous-time and discrete-time solvers for network linear equations, and prove their linear convergence properties under scalar node communications. With scalar communications, a direct benefit would be the reduced node-to-node communication channel burden for distributed computing. Numerical examples are presented to illustrate the effectiveness of the established theoretical results.

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Lei Wang, Zihao Ren, Deming Yuan, Guodong Shi. 2024-01-12. Distributed Solvers for Network Linear Equations with Scalarized Compression. https://arxiv.org/abs/2401.06332

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