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arXiv 2203.01271math.OC
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随机纳什博弈中稳定价格估计的随机逼近方法

Stochastic Approximation for Estimating the Price of Stability in Stochastic Nash Games

Afrooz Jalilzadeh, Farzad Yousefian, Mohammadjavad Ebrahimi

AI总结:

本文针对随机纳什博弈中稳定价格(PoS)难以高效计算的问题,提出基于随机逼近的求解方案,并给出近似误差界,为网络化系统效率评估提供了新方法。

摘要:

本文的目标是利用随机逼近(SA)方案来近似随机纳什博弈中的稳定价格(PoS)。PoS 是博弈论中最常用的指标之一,为估计纳什博弈的效率提供了一种途径。特别是,了解 PoS 的值有助于设计高效的网络化系统,包括交通网络和电力市场机制。由于缺乏计算 PoS 的有效方法,我们首先考虑具有非光滑且仅凸的目标函数以及仅单调的随机变分不等式(SVI)约束的随机优化问题。该问题出现在 PoS 比值的分子中。我们开发了一种随机块坐标随机额外(次)梯度方法,其中采用了一种新颖的迭代惩罚方案,以在算法的两次梯度更新中分别考虑 SVI 的映射。我们获得了阶为 $ε^{-4}$ 的迭代复杂度,这似乎是这类带约束随机优化问题中已知最好的结果,其中 $ε$ 表示对适当定义的不可行性和次优性度量的任意界限。其次,我们开发了一种基于 SA 的方案来近似 PoS,并推导出近似误差的下界和上界。为了验证理论发现,我们提供了网络化随机纳什古诺竞争上的初步仿真结果。

英文摘要:

The goal in this paper is to approximate the Price of Stability (PoS) in stochastic Nash games using stochastic approximation (SA) schemes. PoS is amongst the most popular metrics in game theory and provides an avenue for estimating the efficiency of Nash games. In particular, knowing the value of PoS can help with designing efficient networked systems, including transportation networks and power market mechanisms. Motivated by the lack of efficient methods for computing the PoS, first we consider stochastic optimization problems with a nonsmooth and merely convex objective function and a merely monotone stochastic variational inequality (SVI) constraint. This problem appears in the numerator of the PoS ratio. We develop a randomized block-coordinate stochastic extra-(sub)gradient method where we employ a novel iterative penalization scheme to account for the mapping of the SVI in each of the two gradient updates of the algorithm. We obtain an iteration complexity of the order $ε^{-4}$ that appears to be best known result for this class of constrained stochastic optimization problems, where $ε$ denotes an arbitrary bound on suitably defined infeasibility and suboptimality metrics. Second, we develop an SA-based scheme for approximating the PoS and derive lower and upper bounds on the approximation error. To validate the theoretical findings, we provide preliminary simulation results on a networked stochastic Nash Cournot competition.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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