Distributed Large Scale Network Utility Maximization
arXiv:0901.2684 · doi:10.1109/ISIT.2009.5205655
Abstract
Recent work by Zymnis et al. proposes an efficient primal-dual interior-point method, using a truncated Newton method, for solving the network utility maximization (NUM) problem. This method has shown superior performance relative to the traditional dual-decomposition approach. Other recent work by Bickson et al. shows how to compute efficiently and distributively the Newton step, which is the main computational bottleneck of the Newton method, utilizing the Gaussian belief propagation algorithm. In the current work, we combine both approaches to create an efficient distributed algorithm for solving the NUM problem. Unlike the work of Zymnis, which uses a centralized approach, our new algorithm is easily distributed. Using an empirical evaluation we show that our new method outperforms previous approaches, including the truncated Newton method and dual-decomposition methods. As an additional contribution, this is the first work that evaluates the performance of the Gaussian belief propagation algorithm vs. the preconditioned conjugate gradient method, for a large scale problem.
In the International Symposium on Information Theory (ISIT) 2009
References in corpus (2)
Cited by in corpus (8)
- Distributed Cross-Layer Optimization in Wireless Networks: A Second-Order Approach
- Distributive Network Utility Maximization (NUM) over Time-Varying Fading Channels
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- Distributed Sensor Selection using a Truncated Newton Method
- Convergence and Accuracy Analysis for A Distributed Static State Estimator based on Gaussian Belief Propagation
- Fault Identification via Non-parametric Belief Propagation
- A Distributed Newton Approach for Joint Multi-Hop Routing and Flow Control: Theory and Algorithm