.png){kind=icon}
.png){kind=icon}
Return
Continuous-Time Algorithm Based on Finite-Time Consensus for Distributed Constrained Convex Optimization
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2021.3079192.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article studies the convex optimization problem with general constraints, where its global objective function is composed of the sum of local objective functions. The objective is to design a distributed algorithm to cooperatively resolve the optimization problem under the condition that only the information of each node's own local cost function and its neighbors' states can be obtained. To this end, the optimality condition of the researched optimization problem is developed in terms of the saddle point theory. On this basis, the corresponding continuous-time primal-dual algorithm is constructed for the considered constrained convex optimization problem under time-varying undirected and connected graphs. In the case that the parameters involved in the proposed algorithm satisfy certain inequality, the states of all nodes will reach consensus in finite time. Meanwhile, the average state is globally convergent to the optimal solution of the considered optimization problem under some mild and standard assumptions.
Keywords:
Optimization
Convex functions
Distributed algorithms
Convergence
Linear programming
Multi-agent systems
Graph theory
Distributed optimization
finite-time consensus
general constraints
primal-dual algorithm
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W