Description
TL;DR: A method for reshaping a network matrix's spectrum so distributed averaging reaches agreement in fewer communication rounds.
About This Document
Effrosyni Kokiopoulou and Pascal Frossard start with linear iterative average-consensus algorithms whose convergence depends on topology and edge weights. They apply a polynomial filter to the network matrix, allowing each sensor to combine several earlier estimates during periodic updates. The filter coefficients are selected through a semidefinite program, and simulations examine the acceleration achieved on both fixed and changing network topologies.
Why Bitcoin People May Care
This predates Bitcoin and is not a blockchain-consensus paper. The nodes are averaging values, not resolving adversarial transaction history or spending scarce work. It remains useful background because topology, message rounds, local computation, and convergence speed are real costs in any distributed network. The comparison works only if those different objectives and fault assumptions stay visible.
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Document Details
- Title: Polynomial Filtering for Faster Distributed Consensus
- Author / organization: Effrosyni Kokiopoulou and Pascal Frossard
- Year: 2008
- Language: English
- Document type: Distributed-systems research paper
- Pages: 18
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