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New Contraction Bounds for Multidimensional Asymptotic Consensus in Dynamic Networks
This work studies the multidimensional asymptotic agreement problem in dynamic networks. We assume that nodes have $d$-dimensional inputs and need to converge arbitrarily close to each other's inputs while their vectors stay inside the convex hull of all inputs. We present a new upper bound of $\s
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