基于连续时间的二阶多智能体分布式资源分配算法

针对二阶多智能体系统中的分布式资源分配问题, 本文设计两种连续时间算法. 基于KKT (Karush?Kuhn?Tucker, 卡罗需?库恩?塔克)优化条件, 第一种控制算法利用节点局部不等式及其梯度信息来约束节点状态. 与上述梯度方法不同, 第二种控制算法包括一致性梯度下降法和固定时间收敛映射算子, 其中固定时间收敛映射算子确保算法的节点状态在固定时间收敛到局部约束集, 一致性梯度下降法目的是确保节点迭代到资源分配问题最优解. 两种控制算法都对状态无初始值约束, 且控制参数都是常数. 利用凸优化理论和固定时间李雅普诺夫方法, 分别分析了上述控制策略在有向平衡网络条件下的渐近和指数收敛性. 最后通过数值仿真验证了所设计算法在一维和高维资源分配问题的有效性.     

基于鞍点法的自适应分布式资源分配算法

研究一类带有不等式约束为凸函数的多智能体系统分布式资源分配问题.在资源分配问题中,各智能体拥有仅自身可知的局部成本函数和局部凸不等式约束.分布式资源分配旨在如何利用智能体间的信息交互设计一种分布式优化算法,完成定量资源分配的同时还保证最小化全局成本函数.针对该问题,基于卡罗需-库恩-塔克条件和比例积分控制思想,首先提出一种自适应分布式优化算法,其中凸不等式约束的对偶变量可实现自适应获取;然后,为了降低系统的通信资源消耗,设计一种动态事件触发控制策略以实现离散时间通信的分布式资源分配算法;最后,通过数值仿真验证所设计算法的有效性.     

基于自适应精确罚函数的分布式资源分配算法

在多智能体系统中, 分布式资源分配问题是近年来研究热点之一. 分布式资源分配问题旨在通过智能体间信息交互实现资源最优配置. 其中智能体局部约束给算法设计带来巨大挑战. 首先, 针对一阶多智能体系统, 提出基于自适应精确罚函数的分布式资源分配算法, 其中各智能体利用距离函数实现局部约束求解. 此外, 自适应设计思想旨在避免算法对全局先验知识获取. 其次, 利用跟踪技术实现二阶多智能体系统算法设计. 并利用凸函数和非光滑分析法给出严谨的收敛性分析. 最后, 仿真结果验证了本文所设计优化算法对强凸分布式资源分配问题的有效性.     

Distributed optimal resource allocation of second‐order multiagent systems

In this paper, the resource allocation problems of multiagent systems are investigated. Different from the well‐studied resource allocation problems, the dynamics of agents are taken into account in our problem, which results that the problem could not be solved by most of existing resource allocation algorithms. Here, the agents are in the form of second‐order dynamics, which causes the difficulties in designing and analyzing distributed resource allocation algorithms. Based on gradient descent and state feedback, two distributed resource allocation algorithms are proposed to achieve the optimal allocation, and their convergence are analyzed by constructing suitable Lyapunov functions. One of the two algorithms can ensure that the decisions of all agents asymptotically converge to the exact optimal solution, and the other algorithm achieves the exponential convergence. Finally, numerical examples about the economic dispatch problems of power grids are given to verify the effectiveness of the obtained results.     

OFDMA中继系统下行链路公平资源分配算法研究*

为了公平有效地在OFDMA中继系统下行链路进行资源分配,提出了基于效用函数的资源分配算法,并根据凸优化问题的原始分解理论,提出了相应的分布式求解算法,得到了相关问题的全局最优解。理论分析和数值仿真结果表明,提出的资源分配算法具有较低的系统复杂度,可以使系统效用函数最大,从而在保证用户公平性的前提下,使系统的和速率容量最大。     

采用随机规划模型的云资源分配算法

针对云资源提供问题,为了降低云消费者的资源使用成本,提出了一种采用随机规划模型的云资源分配算法.同时考虑按需实例和预留实例,采用两阶段随机整数规划对云资源提供问题进行建模,在资源预留阶段,根据长期的工作负载情况,确定预留实例的类型和数量,在按需分配阶段,根据当前的工作负载,确定动态分配的按需实例的类型和数量.采用抽样平均近似方法减少资源提供问题的场景数量,降低求解复杂度,并提出了一种基于阶段分解的混合进化算法求解资源提供问题.仿真实验结果表明,采用随机规划模型的云资源分配算法能够在较短时间内获得近似最优的云资源预留方案,有效降低了云消费者的资源使用成本.     

二阶智能体的分布式非光滑资源分配算法

分布式资源分配问题旨在满足局部约束下完成一定量资源分配的同时使全局成本函数最小.首先,针对无向连通网络下二阶积分器型线性智能体系统,结合Karush-Kuhn-Tucker条件,提出一种初始值任意的分布式优化算法,其中,全局等式约束对偶变量实现比例积分控制,局部凸函数不等式约束对偶变量实现自动获取.当全局成本函数为非光滑凸函数时,借助集值LaSalle不变性原理理论证明所提出算法渐近收敛到全局最优解.其次,将所提出算法推广至无向连通网络下参数未知的Euler-Lagrange多智能体系统.当全局成本函数为非光滑凸函数时,借助Barbalat引理理论证明所提出算法渐近收敛到全局最优解.最后,通过数值仿真验证了所提算法的有效性.     

Two stochastic optimization algorithms for convex optimization with fixed point constraints

Two optimization algorithms are proposed for solving a stochastic programming problem for which the objective function is given in the form of the expectation of convex functions and the constraint set is defined by the intersection of fixed point sets of nonexpansive mappings in a real Hilbert space. This setting of fixed point constraints enables consideration of the case in which the projection onto each of the constraint sets cannot be computed efficiently. Both algorithms use a convex function and a nonexpansive mapping determined by a certain probabilistic process at each iteration. One algorithm blends a stochastic gradient method with the Halpern fixed point algorithm. The other is based on a stochastic proximal point algorithm and the Halpern fixed point algorithm; it can be applied to nonsmooth convex optimization. Convergence analysis showed that, under certain assumptions, any weak sequential cluster point of the sequence generated by either algorithm almost surely belongs to the solution set of the problem. Convergence rate analysis illustrated their efficiency, and the numerical results of convex optimization over fixed point sets demonstrated their effectiveness.     

Stochastic robustness metric and its use for static resource allocations

This research investigates the problem of robust static resource allocation for distributed computing systems operating under imposed Quality of Service (QoS) constraints. Often, such systems are expected to function in a physical environment replete with uncertainty, which causes the amount of processing required to fluctuate substantially over time. Determining a resource allocation that accounts for this uncertainty in a way that can provide a probabilistic guarantee that a given level of QoS is achieved is an important research problem. The stochastic robustness metric proposed in this research is based on a mathematical model where the relationship between uncertainty in system parameters and its impact on system performance are described stochastically.The utility of the established metric is then exploited in the design of optimization techniques based on greedy and iterative approaches that address the problem of resource allocation in a large class of distributed systems operating on periodically updated data sets. The performance results are presented for a simulated environment that replicates a heterogeneous cluster-based radar data processing center. A mathematical performance lower bound is presented for comparison analysis of the heuristic results. The lower bound is derived based on a relaxation of the Integer Linear Programming formulation for a given resource allocation problem.     

Tradeoff between utility and lifetime in energy-constrained wireless sensor networks

We study the tradeoff between network utility and network lifetime using a cross-layer optimization approach. The tradeoff model in this paper is based on the framework of layering as optimization decomposition. Our tradeoff model is the first one that incorporates time slots allocation into this framework. By using Lagrangian dual decomposition method, we decompose the tradeoff model into two subproblems: routing problem at network layer and resource allocation problem at medium access control (MAC) layer. The interfaces between the layers are precisely the dual variables. A partially distributed algorithm is proposed to solve the nonlinear, convex, and separable tradeoff model. Numerical simulation results are presented to support our algorithm.     

非平衡有向网络的完全分布式凸优化

分布式凸优化问题的目的是如何以分布式方法最小化局部智能体成本函数和,而现有分布式算法的控制步长选取依赖于系统智能体个数、伴随矩阵等全局性信息,有悖于分布式算法的初衷.针对此问题,提出一种基于非平衡有向网络的完全分布式凸优化算法(FDCOA).基于多智能体一致性理论和梯度跟踪技术,设计了一种非负余量迭代策略,使得FDCOA的控制步长收敛范围仅与智能体局部信息相关,进而实现控制步长的分布式设置.进一步分析了FDCOA在固定强连通和时变强连通网络情形下的收敛性.仿真结果表明本文构建的分布式控制步长选取方法对FDCOA在有向非平衡下的分布式凸优化问题是有效的.     

On reduction of the two-stage problem of quantile optimization to the problem of convex programming

Consideration was given to the two-stage problem of stochastic programming with a quantile criterion. The case of bilinear loss function which is linear separately in the normally distributed random factors and the strategies was studied. An algorithm was proposed based on solving the parametric problem of convex programming with the scalar parameter selected with the use of the dichotomy method. The solution proved to be guaranteeing for the original problem. An example was discussed.     

基于李雅普诺夫随机优化的车辆边缘计算资源管理

针对车辆边缘计算系统中的计算资源管理问题,提出一种基于李雅普诺夫随机优化的计算卸载与资源分配方案.构建在保证任务量及长期能耗约束下的车辆用户服务时延最小化优化问题,利用李雅普诺夫随机优化理论将优化问题分解.在本地计算资源分配子问题中,通过求解线性问题的方法,得到最优本地计算CPU频率;在计算卸载子问题中,利用数值优化求...     

车联网D2D通信中最大化频谱资源利用率分配算法

无线通信技术快速发展,终端设备不断增多,为缓解这一现象,提升系统网络容量,针对车联网蜂窝D2D(device to device)通信资源分配问题,提出了一种最大化频谱资源利用率分配算法.该算法以最大化频谱资源利用率为优化目标,在满足车联网通信的基本服务质量(quality of service,QoS)下,通过V2V(vehicle to vehi-cle)和V2P(vehicle to people)共享信道资源来提高频谱资源利用率.首先利用信道状态信息定义的链路增益因子为终端用户找到潜在的通信链路集合;然后证明终端用户复用链路资源时功率分配问题为一个凸优化问题,利用凸优化理论求得最优传输功率;随后求解最优的信道匹配问题,此问题为多对一的加权匹配问题,为降低算法复杂度用KM(Kuhn Munkres)算法来求解.仿真结果表明,所提算法较其他算法能够有效地提升系统吞吐量、提高频谱资源利用率、提升网络性能,优化车联网通信资源分配问题.     

Resource Allocation Based on User Pairing and Subcarrier Matching for Downlink Non-Orthogonal Multiple Access Networks

The traditional orthogonal multiple access (OMA) is unable to satisfy the needs of large number of smart devices. To increase the transmission rate in the limited spectrum resource, implementation of both non-orthogonal multiple access (NOMA) and successive interference cancelation (SIC) is essential. In this paper, an optimal resource allocation algorithm in NOMA is proposed to maximize the total system rate in a multi-sector multi-subcarrier relay-assisted communication network. Since the original problem is a non-convex problem with mixed integer programming which is non-deterministic polynomial-time (NP)-hard, a three-step solution is proposed to solve the primal problem. Firstly, we determine the optimal power allocation of the outer users by using the approach of monotonic discrimination, and then the optimal user pairing is determined. Secondly, the successive convex approximation (SCA) method is introduced to transform the non-convex problem involving central users into convex one, and the Lagrangian dual method is used to determine the optimal solution. Finally, the standard Hungarian algorithm is utilized to determine the optimal subcarrier matching. The simulation results show that resource allocation algorithm is able to meet the user performance requirements with NOMA, and the total system rate is improved compared to the existing algorithms.      

基于扩散方法的分布式随机变分推断算法

分布式网络上的聚类、估计或推断具有广泛的应用,因此引起了许多关注.针对己有的分布式变分贝叶斯(Vari-ational Bayesian,VB)算法效率低,可扩展性差的问题,本文借用扩散方法提出了一种新的分布式随机变分推断(Stochastic variational inference,SVI)算法,其中我们选择自然...     

多用户分布式MIMO-OFDM系统的多维资源分配算法

针对多用户分布式MIMO-OFDM系统中的资源分配问题,结合分布式架构特点,提出了一种基于分级优化的天线、子载波与功率联合分配算法.该算法将三维的资源联合分配问题分级转换为两次二维资源联合分配问题,即先引入端口并行处理机制,完成天线与子载波的分配,形成"用户-子信道对",进而采用注水功率分配的方式,完成功率在"用户-子...     

无线多媒体通信网适应带宽配置在线优化算法

基于强化学习的方法,提出一种无线多媒体通信网适应带宽配置在线优化算法,在满足多类业务不同QoS(quality of service)要求的同时,提高网络资源的利用率.建立事件驱动的随机切换分析模型,将无线多媒体通信网中的适应带宽配置问题转化为带约束的连续时间Markov决策问题.利用此模型的动态结构特性,结合在线学习估计梯度与随机逼近改进策略,提出适应带宽配置在线优化算法.该算法不依赖于系统参数,如呼叫到达率、呼叫持续时间等,自适应性强,计算量小,能够收敛到全局最优,适用于复杂应用环境中无线多媒体通信网适应带宽配置的在线优化.仿真实验结果验证了算法的有效性.     

Random gradient-free method for online distributed optimization with strongly pseudoconvex cost functions

This paper focuses on the online distributed optimization problem based on multi-agent systems. In this problem, each agent can only access its own cost function and a convex set, and can only exchange local state information with its current neighbors through a time-varying digraph. In addition, the agents do not have access to the information about the current cost functions until decisions are made. Different from most existing works on online distributed optimization, here we consider the case where the cost functions are strongly pseudoconvex and real gradients of the cost functions are not available. To handle this problem, a random gradient-free online distributed algorithm involving the multi-point gradient estimator is proposed. Of particular interest is that under the proposed algorithm, each agent only uses the estimation information of gradients instead of the real gradient information to make decisions. The dynamic regret is employed to measure the proposed algorithm. We prove that if the cumulative deviation of the minimizer sequence grows within a certain rate, then the expectation of dynamic regret increases sublinearly. Finally, a simulation example is given to corroborate the validity of our results.     

Distributed resource allocation of second‐order multiagent systems with exogenous disturbances

In this paper, we study the resource allocation problem of second‐order multiagent systems with exogenous disturbances, and the communication networks are weight‐balanced digraphs. Different from the well‐studied resource allocation problems, our problem involves the disturbed second‐order dynamics of agents. In order to achieve the optimal allocation, we propose a distributed algorithm based on gradient descent and internal model approach. Furthermore, we analyze the convergence of the algorithm by constructing a suitable Lyapunov function. Moreover, we prove that the agents in the network can achieve the exact optimal allocation even in the presence of external disturbances. Finally, we provide two examples to illustrate our result.