arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2208.04446math.OCcs.SYeess.SY
收藏

求解网络效用最大化问题的安全对偶梯度法

Safe Dual Gradient Method for Network Utility Maximization Problems

Berkay Turan, Mahnoosh Alizadeh

摘要:

在本文中,我们介绍了一种新的一阶双梯度算法解决网络效用最大化问题,出现在网络的资源分配方案与安全关键的约束。受客户需求只能通过公布的价格而无法与客户进行实时双向通信的应用程序的启发,我们需要一种算法来生成\textit{safe prices}。这意味着在任何迭代中,响应于公布价格的已实现需求都不应该违反网络的安全约束。因此,与现有的一阶方法相比,我们的算法,称为安全的双梯度法(SDGM),保证在所有迭代产生可行的原始迭代。我们确保原始的可行性,1)增加了一个递减的安全裕度的约束,2)使用基于符号的双重更新方法与不同的步长的正负方向。此外,我们证明了由SDGM产生的原始迭代实现了${\cal O}(\sqrt{T})$的次线性静态遗憾。

英文摘要:

In this paper, we introduce a novel first-order dual gradient algorithm for solving network utility maximization problems that arise in resource allocation schemes over networks with safety-critical constraints. Inspired by applications where customers' demand can only be affected through posted prices and real-time two-way communication with customers is not available, we require an algorithm to generate \textit{safe prices}. This means that at no iteration should the realized demand in response to the posted prices violate the safety constraints of the network. Thus, in contrast to existing first-order methods, our algorithm, called the safe dual gradient method (SDGM), is guaranteed to produce feasible primal iterates at all iterations. We ensure primal feasibility by 1) adding a diminishing safety margin to the constraints, and 2) using a sign-based dual update method with different step sizes for plus and minus directions. In addition, we prove that the primal iterates produced by the SDGM achieve a sublinear static regret of ${\cal O}(\sqrt{T})$.

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。

↑