Quantum mechanics

Algorithm

Event (particle physics)

Convergence (economics)

Constraint (computer-aided design)

Economics

Set (abstract data type)

Subgradient method

Computer science

Mathematical optimization

Projection (relational algebra)

Distributed computing

Physics

Mathematics

Enhanced Data Rates for GSM Evolution

Artificial intelligence

Geometry

Programming language

Economic growth

Machine learning

State (computer science)

IEEE Transactions on Automatic Control

This paper proposes a distributed subgradient method for constrained optimization with event-triggered communications. In the proposed method, each agent has an estimate of an optimal solution as a state and iteratively updates it by a consensus-based subgradient algorithm with a projection to a common constraint set. The local communications are carried out by the edge-based triggering mechanism when the difference between the current state and the last triggered state exceeds a threshold. We show that the states of all agents asymptotically converge to one of the optimal solutions under a diminishing and summability condition on a stepsize and a threshold for a trigger condition. We also investigate the convergence rate with respect to the time-averaged state of each agent. The simulation results show that the proposed event-triggered algorithm can reduce the number of communications compared to the time-triggered algorithms.

Distributed Subgradient Method With Edge-Based Event-Triggered Communication