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This research aims to enhance average consensus algorithms used in distributed computation systems.

June 28, 2026Applied Network ScienceOpen Access

On fast and exact average consensus algorithms

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This research aims to enhance average consensus algorithms used in distributed computation systems.
Proposed new iterative and exact extensions for average consensus algorithms.
Generalized eigenstep method to enable finite-time convergence on directed graphs.
Developed an exact algorithm for linear constraints, achieving O(N) complexity.
New algorithm requires O(N) additions, significantly improving efficiency over existing methods.
Compared to traditional schemes that need O(KN^2) multiplications, the proposed methods achieve faster consensus.
Generalization shows promise for directed graphs' performance in average consensus.

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Randomized trial investigates average consensus algorithms in distributed networks, suggesting new iterative approaches.

Ricardo Merched (2026) studied this question.

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