非齐次位置相关马尔可夫链,非同步目标和优化

F. Wirth, S. Stüdli, Jia Yuan Yu, M. Corless, R. Shorten
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引用次数: 12

摘要

针对一类优化问题,提出了一种随机算法,该算法是在一组智能体以最优方式竞争共享单个约束资源时出现的。该方法使用间歇的单比特反馈,它指示约束违反并且不需要代理间通信。该算法基于AIMD的正矩阵模型,并将其推广到非齐次马尔可夫情况下。关键特征是将退出概率分配给单个代理,作为过去对资源的平均访问的函数。这导致了扩展状态空间中的非齐次马尔可夫链,并证明了接近社会最优的平均路径几乎肯定收敛。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonhomogeneous Place-dependent Markov Chains, Unsynchronised AIMD, and Optimisation
A stochastic algorithm is presented for a class of optimisation problems that arise when a group of agents compete to share a single constrained resource in an optimal manner. The approach uses intermittent single-bit feedback, which indicates a constraint violation and does not require inter-agent communication. The algorithm is based on a positive matrix model of AIMD, which is extended to the nonhomogeneous Markovian case. The key feature is the assignment of back-off probabilities to the individual agents as a function of the past average access to the resource. This leads to a nonhomogeneous Markov chain in an extended state space, and we show almost sure convergence of the average access to the social optimum.
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