This paper proposes second-order distributed algorithms over multi-agent networks to solve the convex optimization problem by utilizing the gradient tracking strategy, with convergence acceleration being achieved. Bot...This paper proposes second-order distributed algorithms over multi-agent networks to solve the convex optimization problem by utilizing the gradient tracking strategy, with convergence acceleration being achieved. Both the undirected and unbalanced directed graphs are considered, extending existing algorithms that primarily focus on undirected or balanced directed graphs. Our algorithms also have the advantage of abandoning the diminishing step-size strategy so that slow convergence can be avoided. Furthermore, the exact convergence to the optimal solution can be realized even under the constant step size adopted in this paper. Finally, two numerical examples are presented to show the convergence performance of our algorithms.展开更多
This paper proposes a distributed continuous-time momentum gradient descent(MGD)algorithm for convex optimization over multi-agent networks,where agents collaboratively minimize the sum of local convex cost functions ...This paper proposes a distributed continuous-time momentum gradient descent(MGD)algorithm for convex optimization over multi-agent networks,where agents collaboratively minimize the sum of local convex cost functions through coordinated communication.First,we establish exponential convergence under ideal continuous-time coordination through Lyapunov analysis.To bridge the gap between theoretical designs and digital implementations,two strategies are developed:(1)a time-triggered control(TTC)scheme that guarantees stability under bounded sampling intervals;(2)a periodic event-triggered control(PETC)strategy.Notably,the PETC strategy is introduced to address the inefficiency in network resource utilization inherent in TTC by activating communication only when necessary.By formulating the PETC-based algorithm as a hybrid dynamical system with event-driven thresholds,we subsequently construct a parameterized hybrid Lyapunov function to rigorously prove the global asymptotic stability of the equilibrium point.Comprehensive numerical experiments confirm the convergence of the algorithm under both strategies,with PETC achieving a reduction in communication frequency compared to TTC,while maintaining solution accuracy.展开更多
Decentralized Online Learning(DOL)extends online learning to the domain of distributed networks.However,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models com...Decentralized Online Learning(DOL)extends online learning to the domain of distributed networks.However,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models compared to centralized methods.Considering the increasing requirement to achieve a high-precision model or decision with distributed data resources in a network,applying ensemble methods is attempted to achieve a superior model or decision with only transferring gradients or models.A new boosting method,namely Boosting for Distributed Online Convex Optimization(BD-OCO),is designed to realize the application of boosting in distributed scenarios.BD-OCO achieves the regret upper bound O(M+N/MNT)where M measures the size of the distributed network and N is the number of Weak Learners(WLs)in each node.The core idea of BD-OCO is to apply the local model to train a strong global one.BD-OCO is evaluated on the basis of eight different real-world datasets.Numerical results show that BD-OCO achieves excellent performance in accuracy and convergence,and is robust to the size of the distributed network.展开更多
We propose a dual decomposition based algorithm that solves the AC optimal power flow(ACOPF) problem in the radial distribution systems and microgrids in a collaborative and distributed manner. The proposed algorithm ...We propose a dual decomposition based algorithm that solves the AC optimal power flow(ACOPF) problem in the radial distribution systems and microgrids in a collaborative and distributed manner. The proposed algorithm adopts the second-order cone program(SOCP) relaxed branch flow ACOPF model. In the proposed algorithm, bus-level agents collaboratively solve the global ACOPF problem by iteratively sharing partial variables with its 1-hop neighbors as well as carrying out local scalar computations that are derived using augmented Lagrangian and primal-dual subgradient methods. We also propose two distributed computing platforms, i. e., high-performance computing(HPC) based platform and hardware-in-theloop(HIL) testbed, to validate and evaluate the proposed algorithm. The computation and communication performances of the proposed algorithm are quantified and analyzed on typical IEEE test systems. Experimental results indicate that the proposed algorithm can be executed on a fully distributed computing structure and yields accurate ACOPF solution. Besides, the proposed algorithm has a low communication overhead.展开更多
基金supported by National Nature Science Foundation of China (Nos. 61663026, 62066026, 61963028 and 61866023)Jiangxi NSF (No. 20192BAB 207025)。
文摘This paper proposes second-order distributed algorithms over multi-agent networks to solve the convex optimization problem by utilizing the gradient tracking strategy, with convergence acceleration being achieved. Both the undirected and unbalanced directed graphs are considered, extending existing algorithms that primarily focus on undirected or balanced directed graphs. Our algorithms also have the advantage of abandoning the diminishing step-size strategy so that slow convergence can be avoided. Furthermore, the exact convergence to the optimal solution can be realized even under the constant step size adopted in this paper. Finally, two numerical examples are presented to show the convergence performance of our algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.08120005)。
文摘This paper proposes a distributed continuous-time momentum gradient descent(MGD)algorithm for convex optimization over multi-agent networks,where agents collaboratively minimize the sum of local convex cost functions through coordinated communication.First,we establish exponential convergence under ideal continuous-time coordination through Lyapunov analysis.To bridge the gap between theoretical designs and digital implementations,two strategies are developed:(1)a time-triggered control(TTC)scheme that guarantees stability under bounded sampling intervals;(2)a periodic event-triggered control(PETC)strategy.Notably,the PETC strategy is introduced to address the inefficiency in network resource utilization inherent in TTC by activating communication only when necessary.By formulating the PETC-based algorithm as a hybrid dynamical system with event-driven thresholds,we subsequently construct a parameterized hybrid Lyapunov function to rigorously prove the global asymptotic stability of the equilibrium point.Comprehensive numerical experiments confirm the convergence of the algorithm under both strategies,with PETC achieving a reduction in communication frequency compared to TTC,while maintaining solution accuracy.
基金This work was supported by the National Natural Science Foundation of China(No.U19B2024)the National Key Research and Development Program(No.2018YFE0207600)。
文摘Decentralized Online Learning(DOL)extends online learning to the domain of distributed networks.However,limitations of local data in decentralized settings lead to a decrease in the accuracy of decisions or models compared to centralized methods.Considering the increasing requirement to achieve a high-precision model or decision with distributed data resources in a network,applying ensemble methods is attempted to achieve a superior model or decision with only transferring gradients or models.A new boosting method,namely Boosting for Distributed Online Convex Optimization(BD-OCO),is designed to realize the application of boosting in distributed scenarios.BD-OCO achieves the regret upper bound O(M+N/MNT)where M measures the size of the distributed network and N is the number of Weak Learners(WLs)in each node.The core idea of BD-OCO is to apply the local model to train a strong global one.BD-OCO is evaluated on the basis of eight different real-world datasets.Numerical results show that BD-OCO achieves excellent performance in accuracy and convergence,and is robust to the size of the distributed network.
基金supported by the National Science Foundation (No. CNS-1505633)。
文摘We propose a dual decomposition based algorithm that solves the AC optimal power flow(ACOPF) problem in the radial distribution systems and microgrids in a collaborative and distributed manner. The proposed algorithm adopts the second-order cone program(SOCP) relaxed branch flow ACOPF model. In the proposed algorithm, bus-level agents collaboratively solve the global ACOPF problem by iteratively sharing partial variables with its 1-hop neighbors as well as carrying out local scalar computations that are derived using augmented Lagrangian and primal-dual subgradient methods. We also propose two distributed computing platforms, i. e., high-performance computing(HPC) based platform and hardware-in-theloop(HIL) testbed, to validate and evaluate the proposed algorithm. The computation and communication performances of the proposed algorithm are quantified and analyzed on typical IEEE test systems. Experimental results indicate that the proposed algorithm can be executed on a fully distributed computing structure and yields accurate ACOPF solution. Besides, the proposed algorithm has a low communication overhead.
