Distributed Event-Triggered Subgradient Method for Convex Optimization With General Step-Size
نویسندگان
چکیده
منابع مشابه
"Efficient" Subgradient Methods for General Convex Optimization
A subgradient method is presented for solving general convex optimization problems, the main requirement being that a strictly-feasible point is known. A feasible sequence of iterates is generated, which converges to within user-specified error of optimality. Feasibility is maintained with a linesearch at each iteration, avoiding the need for orthogonal projections onto the feasible region (an ...
متن کاملDistributed Stochastic Subgradient Projection Algorithms for Convex Optimization
We consider a distributed multi-agent network system where the goal is to minimize a sum of convex objective functions of the agents subject to a common convex constraint set. Each agent maintains an iterate sequence and communicates the iterates to its neighbors. Then, each agent combines weighted averages of the received iterates with its own iterate, and adjusts the iterate by using subgradi...
متن کاملIncremental Stochastic Subgradient Algorithms for Convex Optimization
This paper studies the effect of stochastic errors on two constrained incremental subgradient algorithms. The incremental subgradient algorithms are viewed as decentralized network optimization algorithms as applied to minimize a sum of functions, when each component function is known only to a particular agent of a distributed network. First, the standard cyclic incremental subgradient algorit...
متن کاملComputational study of the step size parameter of the subgradient optimization method
The subgradient optimization method is a simple and flexible linear programming iterative algorithm. It is much simpler than Newton’s method and can be applied to a wider variety of problems. It also converges when the objective function is nondifferentiable. Since an efficient algorithm will not only produce a good solution but also take less computing time, we always prefer a simpler algorith...
متن کاملA class of distributed optimization methods with event-triggered communication
We present a class of methods for distributed optimization with event-triggered communication. To this end, we extend Nesterov’s first order scheme to use event-triggered communication in a networked environment. We then apply this approach to generalize the proximal center algorithm (PCA) for separable convex programs by Necoara and Suykens. Our method uses dual decomposition and applies the d...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2964324