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This paper considers the problem of distributed optimization over time-varying undirected graphs. We discuss a distributed algorithm, which we call DIGing, for solving this problem based on a combination of an inexact gradient method and a gradient tracking technique. This algorithm deploys fixed step size but converges exactly to the global and consensual minimizer. Under strong convexity assumption,...
We study the problem of cooperative learning with a network of agents where some agents repeatedly access information about a random variable with unknown distribution. The group objective is to globally agree on a joint hypothesis (distribution) that best describes the observed data at all nodes. The agents interact with their neighbors in an unknown sequence of time-varying directed graphs. Following...
This paper investigates control laws allowing mobile, autonomous agents to optimally position themselves on the line for distributed sensing in a nonuniform field. We show that a simple static control law, based only on local measurements of the field by each agent, drives the agents to the optimal positions in time that is quadratic in the number of agents. Further, we exhibit a dynamic control law...
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