部分线性化分解：多智能体系统的并行优化
Decomposition by Partial Linearization: Parallel Optimization of Multi-Agent Systems
摘要：
我们提出了一种新的分解框架，分布式优化的一般非凸和效用函数自然产生的无线多用户干扰系统的系统设计。我们的主要贡献是：i）开发具有可证明收敛性的第一类（不精确的）Jacobi最佳响应算法，其中所有用户同时且迭代地解决原始效用和优化问题的适当凸化版本; ii）推导出一般动态定价机制，其提供了对现有定价方案的统一看法，而不是基于竞争力;和iii）一个框架，可以很容易地具体化到众所周知的应用，产生非常有效的实用（Jacobi或Gauss-Seidel）算法，其优于针对非常特定的问题提出的现有adhoc方法。有趣的是，我们的框架包含作为特殊情况下著名的梯度算法的非凸和效用问题，和许多块坐标下降计划凸函数。
英文摘要：
We propose a novel decomposition framework for the distributed optimization of general nonconvex sum-utility functions arising naturally in the system design of wireless multiuser interfering systems. Our main contributions are: i) the development of the first class of (inexact) Jacobi best-response algorithms with provable convergence, where all the users simultaneously and iteratively solve a suitably convexified version of the original sum-utility optimization problem; ii) the derivation of a general dynamic pricing mechanism that provides a unified view of existing pricing schemes that are based, instead, on heuristics; and iii) a framework that can be easily particularized to well-known applications, giving rise to very efficient practical (Jacobi or Gauss-Seidel) algorithms that outperform existing adhoc methods proposed for very specific problems. Interestingly, our framework contains as special cases well-known gradient algorithms for nonconvex sum-utility problems, and many blockcoordinate descent schemes for convex functions.
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