Asynchronous Gossip for Averaging and Spectral Ranking

作者：Borkar Vivek S^*; Makhijani Rahul; Sundaresan Rajesh

来源：IEEE Journal of Selected Topics in Signal Processing, 2014, 8(4): 703-716.

DOI：10.1109/JSTSP.2014.2320229

• Gossip algorithm
• asynchronous stochastic approximation
• Poisson equation
• learning
• Perron-Frobenius eigenvector
• ranking

We consider two variants of the classical gossip algorithm. The first variant is a version of asynchronous stochastic approximation. We highlight a fundamental difficulty associated with the classical asynchronous gossip scheme, viz., that it may not converge to a desired average, and suggest an alternative scheme based on reinforcement learning that has guaranteed convergence to the desired average. We then discuss a potential application to a wireless network setting with simultaneous link activation constraints. The second variant is a gossip algorithm for distributed computation of the Perron-Frobenius eigenvector of a nonnegative matrix. While the first variant draws upon a reinforcement learning algorithm for an average cost controlled Markov decision problem, the second variant draws upon a reinforcement learning algorithm for risk-sensitive control. We then discuss potential applications of the second variant to ranking schemes, reputation networks, and principal component analysis.

• 出版日期2014-8