.png){kind=icon}
.png){kind=icon}
Return
Approximate Analysis of Distributed Dual Decomposition Algorithm for Mixed Integer Linear Programming
.png){kind=icon}
PRE
AI
DOI：10.1587/transfun.2025map0002.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a distributed dual decomposition algorithm for solving mixed-integer linear programming (MILP) problems in multi-agent systems. In the proposed approach, a MILP problem is transformed into an approximated problem by linear programming relaxation, which enables each agent to independently solve their local subproblems. The theoretical analysis provides guarantees on both the feasibility error bounds and the optimality gap of the solutions obtained by the proposed method. The effectiveness of the proposed method is shown through a numerical example of a fairness-aware multiple traveling salesman problem.
Keywords:
distributed optimization
multi-agent systems
mixed integer linear programming
Journal
IF:
0.4
Papers:
210
Citations:
1.3K