Arbitrary sequences of single row or column normalizations converge to a doubly stochastic matrix that depends only on the positive supported part of the initial matrix, enabling a decentralized random walk algorithm with uniform stationary distribution.
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From Local Updates to Global Balance: A Framework for Distributed Matrix Scaling
Arbitrary sequences of single row or column normalizations converge to a doubly stochastic matrix that depends only on the positive supported part of the initial matrix, enabling a decentralized random walk algorithm with uniform stationary distribution.