Abstract
For a group of networked agents, f-consensus means reaching consensus upon the value of a desired function, f, of the initial state of the individual agents. This paper shows how one can often convert a given f-consensus problem into a suitable distributed convex optimization (DCO) problem, which can be readily solved with existing DCO algorithms in the literature. A computational advantage may then accrue. Particular classes of f-consensus problems shown to be solvable with this approach include weighted power mean consensus, and kth smallest value or kth order statistic consensus (which includes max/min consensus and median consensus as special cases).
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