distributed stochastic optimization problem with equality constraints over a random network with imperfect communications is considered, where the measurements of all functions to be used are subject to noises. The go...
详细信息
ISBN:
(纸本)9789881563804
distributed stochastic optimization problem with equality constraints over a random network with imperfect communications is considered, where the measurements of all functions to be used are subject to noises. The goal of the network is to minimize a global cost function subject to a global constraint set, where the global objective is a sum of objective functions, the global constraint set is the intersection of local constraint sets with equality constraints, and each agent only has access to information on its cost function and constraint function. A primal-dual projection-free distributedalgorithm is proposed to solve the problem, where each agent updates its estimates by using the local observations and local information on its neighbors' broadcast state values. Almost sure convergence of the algorithm is proven under mild conditions.
This study investigates distributed convex optimisation that accounts for the inequality constraints over unbalanced directed graphs. In distributed optimisation, agents exchange information on a network to obtain an ...
详细信息
This study investigates distributed convex optimisation that accounts for the inequality constraints over unbalanced directed graphs. In distributed optimisation, agents exchange information on a network to obtain an optimal solution when they only know their own cost function. We propose a distributedprimal-dual subgradient method based on a row stochastic weight matrix that is associated with a communication network. In the proposed method, the normalised left eigenvector of the weight matrix is estimated by a consensus algorithm. Then, the subgradient of the Lagrange function is scaled by the estimated values of the left eigenvector to compensate for the imbalance of the information flow in the network. We show that the pair of estimations for the primal and dual problems converge to an optimal primal-dual solution. We also show the relation between the convergence rate of the proposed algorithm and the step-size rule. A numerical example confirms the validity of the proposed method.
distributed stochastic optimization problem with equality constraints over a random network with imperfect communications is considered, where the measurements of all functions to be used are subject to noises. The go...
详细信息
distributed stochastic optimization problem with equality constraints over a random network with imperfect communications is considered, where the measurements of all functions to be used are subject to noises. The goal of the network is to minimize a global cost function subject to a global constraint set, where the global objective is a sum of objective functions, the global constraint set is the intersection of local constraint sets with equality constraints, and each agent only has access to information on its cost function and constraint function. A primal-dual projection-free distributedalgorithm is proposed to solve the problem, where each agent updates its estimates by using the local observations and local information on its neighbors' broadcast state values. Almost sure convergence of the algorithm is proven under mild conditions.
In this paper, we consider a multi-agent convex optimization problem whose goal is to minimize a global convex objective function that is the sum of local convex objective functions, subject to global convex inequalit...
详细信息
In this paper, we consider a multi-agent convex optimization problem whose goal is to minimize a global convex objective function that is the sum of local convex objective functions, subject to global convex inequality constraints and several randomly occurring local convex state constraint sets. A distributedprimal-dual random projection subgradient (DPDRPS) algorithm with diminishing stepsize using local communications and computations is proposed to solve such a problem. By employing iterative inequality techniques, the proposed DPDRPS algorithm is proved to be convergent almost surely. Finally, a numerical example is illustrated to show the effectiveness of the theoretical analysis. (C) 2016 Elsevier B.V. All rights reserved.
暂无评论