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Most existing work uses dual decomposition and subgradient methods to solve network optimization problems in a distributed manner, which suffer from slow convergence rate properties. This paper proposes an alternative distributed approach based on a Newton-type method for solving minimum cost network optimization problems. The key component of the method is to represent the dual Newton direction as...
A wireless packet network is considered in which each user transmits a stream of packets to its destination. The transmit power of each user interferes with the transmission of all other users. A convex cost function of the completion times of the user packets are minimized by optimally allocating the users' transmission power subject to their respective power constraints. It is shown that, at all...
We study a distributed computation model for optimizing a sum of convex objective functions corresponding to multiple agents. For solving this (not necessarily smooth) optimization problem, we consider a subgradient method that is distributed among the agents. The method involves every agent minimizing his/her own objective function while exchanging information locally with other agents in the network...
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