This paper studies distributed convex optimization over a multi-agent system,where each agent owns only a local cost function with convexity and Lipschitz continuous gradients.The goal of the agents is to cooperativel...This paper studies distributed convex optimization over a multi-agent system,where each agent owns only a local cost function with convexity and Lipschitz continuous gradients.The goal of the agents is to cooperatively minimize a sum of the local cost functions.The underlying communication networks are modelled by a sequence of random and balanced digraphs,which are not required to be spatially or temporally independent and have any special distributions.The authors use a distributed gradient-tracking-based optimization algorithm to solve the optimization problem.In the algorithm,each agent makes an estimate of the optimal solution and an estimate of the average of all the local gradients.The values of the estimates are updated based on a combination of a consensus method and a gradient tracking method.The authors prove that the algorithm can achieve convergence to the optimal solution at a geometric rate if the conditional graphs are uniformly strongly connected,the global cost function is strongly convex and the step-sizes don’t exceed some upper bounds.展开更多
Domain decomposition method and multigrid method can be unified in the framework of the space decomposition method. This paper has obtained a new result on the convergence rate of the space decomposition method, which...Domain decomposition method and multigrid method can be unified in the framework of the space decomposition method. This paper has obtained a new result on the convergence rate of the space decomposition method, which can be applied to some nonuniformly elliptic problems.展开更多
基金supported by the Basic Research Project of Shanghai Science and Technology Commission under Grant No.20JC1414000。
文摘This paper studies distributed convex optimization over a multi-agent system,where each agent owns only a local cost function with convexity and Lipschitz continuous gradients.The goal of the agents is to cooperatively minimize a sum of the local cost functions.The underlying communication networks are modelled by a sequence of random and balanced digraphs,which are not required to be spatially or temporally independent and have any special distributions.The authors use a distributed gradient-tracking-based optimization algorithm to solve the optimization problem.In the algorithm,each agent makes an estimate of the optimal solution and an estimate of the average of all the local gradients.The values of the estimates are updated based on a combination of a consensus method and a gradient tracking method.The authors prove that the algorithm can achieve convergence to the optimal solution at a geometric rate if the conditional graphs are uniformly strongly connected,the global cost function is strongly convex and the step-sizes don’t exceed some upper bounds.
基金the National Natural Science Foundation of China (No.19771034).
文摘Domain decomposition method and multigrid method can be unified in the framework of the space decomposition method. This paper has obtained a new result on the convergence rate of the space decomposition method, which can be applied to some nonuniformly elliptic problems.
