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VI-constrained hemivariational inequalities: distributed algorithms and power control in ad-hoc networks

MATHEMATICAL PROGRAMMING 2014年第1-2期145卷 59-96页

作者： Facchinei, Francisco Pang, Jong-Shi Scutari, Gesualdo Lampariello, Lorenzo Univ Roma La Sapienza Dept Comp Control & Management Engn Antonio Ruber I-00185 Rome Italy Univ Illinois Dept Ind & Enterprise Syst Engn Urbana IL USA SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA

We consider centralized and distributed algorithms for the numerical solution of a hemivariational inequality (HVI) where the feasible set is given by the intersection of a closed convex set with the solution set of a lower-level monotone variational inequality (VI). The algorithms consist of a main loop wherein a sequence of one-level, strongly monotone HVIs are solved that involve the penalization of the non-VI constraint and a combination of proximal and Tikhonov regularization to handle the lower-level VI constraints. Minimization problems, possibly with nonconvex objective functions, over implicitly defined VI constraints are discussed in detail. The methods developed in the paper are then used to successfully solve a new power control problem in ad-hoc networks.

关键词： Hemivariational inequality Hierarchical optimization distributed algorithms Penalization Power control Ad-hoc networks

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Bilinear Expectation Propagation for distributed Semi-Blind Joint Channel Estimation and Data Detection in Cell-Free Massive MIMO

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2024年 5卷 284-293页

作者： Karataev, Alexander Forsch, Christian Cottatellucci, Laura Friedrich Alexander Univ Erlangen Nurnberg Inst Digital Commun D-91054 Erlangen Germany

We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink transmission and propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD). We formulate the problem in the framework of bilinear inference and develop a solution based on the expectation propagation (EP) method for both channel estimation and data detection. We propose a new approximation of the joint a posteriori distribution of the channel and data whose representation as a factor graph enables the application of the EP approach using the message-passing technique, local low-complexity computations at the nodes, and an effective modeling of channel-data interplay. The derived algorithm, called bilinear-EP JCD, allows for a distributed implementation among access points (APs) and the central processing unit (CPU) and has polynomial complexity. Our simulation results show that it outperforms other EP-based state-of-the-art polynomial time algorithms.

关键词： Expectation propagation bilinear inference Bayesian learning approximate inference distributed algorithms joint channel estimation and data detection cell-free massive MIMO

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distributed Optimization With Coupling Constraints

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第3期68卷 1847-1854页

作者： Wu, Xuyang Wang, He Lu, Jie KTH Royal Inst Technol Div Decis & Control Syst SE-10044 Stockholm Sweden ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

In this article, we investigate distributed convex opti-mization with both inequality and equality constraints, where the objective function can be a general nonsmooth convex function and all the constraints can be both sparsely and densely cou-pling. By strategically integrating ideas from primal-dual, proxi-mal, and virtual-queue optimization methods, we develop a novel distributed algorithm, referred to as IPLUX, to address the prob-lem over a connected, undirected graph. We show that IPLUX achieves an O(1/k) rate of convergence in terms of optimality and feasibility, which is stronger than the convergence results of the alternative methods and eliminates the standard assumption on the compactness of the feasible region. Finally, IPLUX exhibits faster convergence and higher efficiency than several state-of-the-art methods in the simulation.

关键词： Convergence Couplings Optimization Convex functions Linear programming distributed algorithms Transforms Constrained optimization distributed optimization primal-dual method proximal algorithm

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A Differentially Private Method for distributed Optimization in Directed Networks via State Decomposition

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2023年第4期10卷 2165-2177页

作者： Chen, Xiaomeng Huang, Lingying He, Lidong Dey, Subhrakanti Shi, Ling Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Nanjing Univ Sci & Technol Sch Automat Nanjing Peoples R China Uppsala Univ Dept Elect Engn S-75103 Uppsala Sweden

In this article, we study the problem of consensus-based distributed optimization, where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In the existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all the agents exchange their states with neighbors to achieve the optimal solution with a constant step size, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition-based gradient tracking approach (SD-Push-Pull) for distributed optimization over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two substates. Only one substate is exchanged by the agent with its neighbors over time, and the other one is not shared. That is to say, only one substate is visible to an adversary, protecting the sensitive information from being leaked. It is proved that under certain decomposition principles, a bound for the suboptimality of the proposed algorithm can be derived, and the differential privacy is achieved simultaneously. Moreover, the tradeoff between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.

关键词： Privacy Optimization Differential privacy Directed graphs Convergence Network systems distributed algorithms Decomposition differentially private directed graph distributed optimization

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Reward Augmentation in Reinforcement Learning for Testing distributed Systems

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PROCEEDINGS OF THE ACM ON PROGRAMMING LANGUAGES-PACMPL 2024年第OOPSLA2期8卷 1928-1954页

作者： Borgarelli, Andrea Enea, Constantin Majumdar, Rupak Nagendra, Srinidhi MPI SWS Kaiserslautern Germany Inst Polytech Paris LIX Ecole Polytech CNRS Palaiseau France Univ Paris Cite IRIF CNRS Paris France Chennai Math Inst Chennai India

Bugs in popular distributed protocol implementations have been the source of many downtimes in popular internet services. We describe a randomized testing approach for distributed protocol implementations based on reinforcement learning. Since the natural reward structure is very sparse, the key to successful exploration in reinforcement learning is reward augmentation. We show two different techniques that build on one another. First, we provide a decaying exploration bonus based on the discovery of new states-the reward decays as the same state is visited multiple times. The exploration bonus captures the intuition from coverage-guided fuzzing of prioritizing new coverage points;in contrast to other schemes, we show that taking the maximum of the bonus and the Q-value leads to more effective exploration. Second, we provide waypoints to the algorithm as a sequence of predicates that capture interesting semantic scenarios. Waypoints exploit designer insight about the protocol and guide the exploration to "interesting" parts of the state space. Our reward structure ensures that new episodes can reliably get to deep interesting states even without execution caching. We have implemented our algorithm in Go. Our evaluation on three large benchmarks (RedisRaft, Etcd, and RSL) shows that our algorithm can significantly outperform baseline approaches in terms of coverage and bug finding.

关键词： CCS Concepts Software and its engineering Software testing and debugging Theory of computation Reinforcement learning Computing methodologies distributed algorithms

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Alternating direction method of multipliers-based distributed control for distributed manipulation by shaping physical force fields

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2023年第1-2期42卷 3-20页

作者： Gurtner, Martin Zemanek, Jiri Hurak, Zdenek Czech Tech Univ Fac Elect Engn Prague Czech Republic

This paper proposes an algorithm for decomposing and possibly distributing an optimization problem that naturally emerges in distributed manipulation by shaping physical force fields through actuators distributed in space (arrays of actuators). One or several manipulated objects located in this field can "feel the force" and move simultaneously and independently. The control system has to produce commands for all actuators so that desired forces are developed at several prescribed places. This can be formulated as an optimization problem that has to be solved in every sampling period. Exploiting the structure of the optimization problem is crucial for platforms with many actuators and many manipulated objects, hence the goal of decomposing the huge optimization problem into several subproblems. Furthermore, if the platform is composed of interconnected actuator modules with computational capabilities, the decomposition can give guidance for the distribution of the computation to the modules. We propose an algorithm for decomposing/distributing the optimization problem using Alternating Direction Method of Multipliers (ADMM). The proposed algorithm is shown to converge to modest accuracy for various distributed platforms in a few iterations. We demonstrate our algorithm through numerical experiments corresponding to three physical experimental platforms for distributed manipulation using electric, magnetic, and pressure fields. Furthermore, we deploy and test it on real experimental platforms for distributed manipulation using an array of solenoids and ultrasonic transducers.

关键词： distributed manipulation distributed algorithms distributed control ADMM

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distributed Design of Wireless Powered Fog Computing Networks With Binary Computation Offloading

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第4期22卷 2084-2099页

作者： Li, Han Xiong, Ke Lu, Yang Gao, Bo Fan, Pingyi Letaief, Khaled Ben Sch Comp & Informat Technol Beijing 100044 Peoples R China Beijing Key Lab Traff Data Anal & Min Beijing 100044 Peoples R China Beijing Jiaotong Univ Frontiers Sci Ctr Smart High Speed Railway Syst Beijing 100044 Peoples R China Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Hong Kong Univ Sci & Technol HKUST Dept Elect & Comp Engn Hong Kong Peoples R China Pengcheng Lab Shenzhen 518055 Guangdong Peoples R China

This paper investigates a multi-user wireless powered fog computing (FC) network, where multiple energy-limited wireless sensor devices (WSDs) first harvest energy from a nearby hybrid access point (HAP), and then compute their tasks locally (i.e., the local computing (LC) mode) or offload the tasks to the HAP (i.e., the FC mode) via a binary offloading policy. In order to pursue the green computing network design, an optimization problem is formulated to minimize the transmit power at the HAP by jointly optimizing the time allocation ratio and the computing mode selection vector, under the energy causality constraints and the WSDs' computing rate requirements constraints. To efficiently solve the formulated non-convex problem in a distributed manner, it is first transformed into an approximate form, and then an alternating direction method of multipliers (ADMM)-based algorithm is designed to solve the transformed problem, based on which the successive convex approximation (SCA) is adopted to improve the approximating precision in an iterative way. With the proposed ADMM-based distributed algorithm, each WSD is able to optimize its computing mode and offloading time with local channel state information (CSI), which thus is more suitable for large-scale networks. For comparison, a channel-sorting-based (CSB) centralized algorithm with global CSI is also presented, and the computational complexities of the proposed ADMM-based algorithm and the CSB algorithm are analyzed. Simulation results show that the proposed distributed algorithm achieves a comparable performance with the CSB centralized algorithm and the exhaustive search method. It is also observed that to minimize the transmit power at the HAP, the WSDs with the better channel quality are inclined to select the LC mode, which is much different from traditional sum-computation-rate maximization design.

关键词： Wireless communication Task analysis Servers Optimization distributed algorithms Approximation algorithms Resource management Fog computing binary offloading wireless power transfer power minimization successive convex approximation alternating direction method of multipliers

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Randomized Gradient-Free distributed Online Optimization via a Dynamic Regret Analysis

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第11期68卷 6781-6788页

作者： Pang, Yipeng Hu, Guoqiang Nanyang Technol Univ Sch Elect & Elect Engn Nanyang 639798 Singapore

This work considers an online distributed optimization problem, with a group of agents whose local objective functions vary with time. Moreover, the value of the objective function is revealed to the corresponding agent after the decision is executed per time-step. Thus, each agent can only update the decision variable based on the revealed value and information collected from the neighbors, without the knowledge on the explicit expression of the objective function. To solve this problem, an online gradient-free distributed projected gradient descent algorithm is presented, where each agent locally approximates the gradient based on two point values. With some standard assumptions on the communication graph and the objective functions, we provide the bound for the dynamic regret as a function of the minimizer path length, step-size, and smoothing parameter. Under appropriate selections of the step-size and smoothing parameter, we prove that the dynamic regret is sublinear with respect to the time duration $T$ if the minimizer path length also grows sublinearly. Finally, the effectiveness of the proposed algorithm is illustrated through numerical simulations.

关键词： distributed algorithms gradient-free techniques online convex optimization

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distributed Algorithm for Nonsmooth Resource Allocation Problems With Nonlinear Constraints and Its Application to Smart Grids

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IEEE SYSTEMS JOURNAL 2023年第2期17卷 3306-3315页

作者： Deng, Zhenhua Chen, Tao Zhao, Yan Cent South Univ Sch Automat Changsha 410075 Peoples R China

In this article, we study nondifferentiable resource allocation problems (RAPs). In our problem, the decisions of agents are subject to coupling inequality constraints, local inequality constraints, and local convex set constraints. In contrast to existing RAPs, the cost functions are nonsmooth and the inequality constraints are nonsmooth and nonlinear in our problem. Based on differential inclusions and projection methods, we exploit a fully distributed subgradient-based resource allocation algorithm to optimally allocate the network resources. With the help of the set-valued LaSalle invariance principle, we prove the global convergence of the algorithm to the optimal resource allocation of our problem. Finally, our method is applied to the economic dispatch problems of smart grids. With our method, the generations of generating units converge to the optimal power generation.

关键词： distributed algorithms multiagent systems nonlinear constraints nonsmooth analysis resource allocation

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distributed and Localized Covariance Control of Coupled Systems: A System Level Approach

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IEEE CONTROL SYSTEMS LETTERS 2025年 9卷 180-185页

作者： Khalil, Ahmed Lee, Yoonjae Bakolas, Efstathios Univ Texas Austin Dept Aerosp Engn & Engn Mech Austin TX 78712 USA

This work is concerned with finite-horizon covariance steering for networked, discrete-time stochastic linear systems with dynamically coupled subsystems, extending beyond existing work limited to decoupled 'agents.' We formulate a localized covariance-steering problem and, using the system & acirc;& Abreve;& Sacute;level synthesis (SLS) framework, recast it as a convex optimization over system responses. After analyzing separability and applying a transformation for nonseparable objectives, we derive a distributed consensus-ADMM algorithm that respects each subsystem's local information and communication constraints. We validate our approach on a power network with 36 interconnected subsystems.

关键词： Noise Convex functions Power system dynamics Covariance matrices Vectors Uncertainty Power grids Mathematical models History distributed algorithms distributed control networked control systems stochastic optimal control

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