Distributed proximal-gradient methods for convex optimization with inequality constraints
MetadataShow full item record
We consider a distributed optimization problem over a multi-agent network, in which the sum of several local convex objective functions is minimized subject to global convex inequality constraints. We first transform the constrained optimization problem to an unconstrained one, using the exact penalty function method. Our transformed problem has a smaller number of variables and a simpler structure than the existing distributed primal–dual subgradient methods for constrained distributed optimization problems. Using the special structure of this problem, we then propose a distributed proximal-gradient algorithm over a time-changing connectivity network, and establish a convergence rate depending on the number of iterations, the network topology and the number of agents. Although the transformed problem is nonsmooth by nature, our method can still achieve a convergence rate, O(1/k) , after k iterations, which is faster than the rate, O(1/ sqrt k), of existing distributed subgradient-based methods. Simulation experiments on a distributed state estimation problem illustrate the excellent performance of our proposed method.
Showing items related by title, author, creator and subject.
Ruan, Ning (2012)Duality is one of the most successful ideas in modern science  . It is essential in natural phenomena, particularly, in physics and mathematics   . In this thesis, we consider the canonical duality ...
Mardaneh, Elham (2010)Many industries are beginning to use innovative pricing techniques to improve inventory control, capacity utilisation, and ultimately the profit of the firm. In manufacturing, the coordination of pricing and production ...
Wang, Y.; Zhou, Guanglu; Zhang, X.; Liu, W.; Caccetta, Louis (2016)The problem of finding a sparse solution for linear equations has been investigated extensively in recent years. This is an NP-hard combinatorial problem, and one popular method is to relax such combinatorial requirement ...