Continuous-Time Algorithm for Multi-Agent Optimization with Global Coupled Constraints

2021 ◽  
Author(s):  
Jiaojiao Yan ◽  
Jinde Cao ◽  
Qingshan Liu
Keyword(s):  
Continuous Time ◽  
Time Algorithm ◽  
Coupled Constraints ◽  
Multi Agent

scholarly journals A projection-based continuous-time algorithm for distributed optimization over multi-agent systems

2021 ◽  
Author(s):  
Xingnan Wen ◽  
Sitian Qin
Keyword(s):  
Continuous Time ◽  
Optimization Problem ◽  
Distributed Optimization ◽  
Optimal Solution ◽  
Fixed Time ◽  
Time Algorithm ◽  
State Variables ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

AbstractMulti-agent systems are widely studied due to its ability of solving complex tasks in many fields, especially in deep reinforcement learning. Recently, distributed optimization problem over multi-agent systems has drawn much attention because of its extensive applications. This paper presents a projection-based continuous-time algorithm for solving convex distributed optimization problem with equality and inequality constraints over multi-agent systems. The distinguishing feature of such problem lies in the fact that each agent with private local cost function and constraints can only communicate with its neighbors. All agents aim to cooperatively optimize a sum of local cost functions. By the aid of penalty method, the states of the proposed algorithm will enter equality constraint set in fixed time and ultimately converge to an optimal solution to the objective problem. In contrast to some existed approaches, the continuous-time algorithm has fewer state variables and the testification of the consensus is also involved in the proof of convergence. Ultimately, two simulations are given to show the viability of the algorithm.

scholarly journals A Continuous-Time Distributed Algorithm for Solving a Class of Decomposable Nonconvex Quadratic Programming

2018 ◽  
Vol 8 (4) ◽  
pp. 283-291 ◽  
Author(s):  
Yan Zhao ◽  
Qingshan Liu
Keyword(s):  
Quadratic Programming ◽  
Continuous Time ◽  
Distributed Algorithm ◽  
Optimal Solution ◽  
Time Algorithm ◽  
Local Information ◽  
Nonconvex Quadratic Programming ◽  
Extra Condition ◽  
Quadratic Programming Problems ◽  
Multi Agent

Abstract In this paper, a continuous-time distributed algorithm is presented to solve a class of decomposable quadratic programming problems. In the quadratic programming, even if the objective function is nonconvex, the algorithm can still perform well under an extra condition combining with the objective, constraint and coupling matrices. Inspired by recent advances in distributed optimization, the proposed continuous-time algorithm described by multi-agent network with consensus is designed and analyzed. In the network, each agent only accesses the local information of its own and from its neighbors, then all the agents in a connected network cooperatively find the optimal solution with consensus.

scholarly journals Improving Computational Efficiency in Crowded Task Allocation Games with Coupled Constraints

2019 ◽  
Vol 9 (10) ◽  
pp. 2117
Author(s):  
Ming Chong Lim ◽  
Han-Lim Choi
Keyword(s):  
Task Allocation ◽  
Random Sampling ◽  
Scheduling Algorithm ◽  
Stochastic Algorithm ◽  
Speed Of Convergence ◽  
Temporal Dependence ◽  
Real World Applications ◽  
Coupled Constraints ◽  
Multi Agent ◽  
Feasible Solutions

Multi-agent task allocation is a well-studied field with many proven algorithms. In real-world applications, many tasks have complicated coupled relationships that affect the feasibility of some algorithms. In this paper, we leverage on the properties of potential games and introduce a scheduling algorithm to provide feasible solutions in allocation scenarios with complicated spatial and temporal dependence. Additionally, we propose the use of random sampling in a Distributed Stochastic Algorithm to enhance speed of convergence. We demonstrate the feasibility of such an approach in a simulated disaster relief operation and show that feasibly good results can be obtained when the confirmation and sample size requirements are properly selected.

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