Hybrid Quantum Benders' Decomposition For Mixed-integer Linear Programming

The Benders' decomposition algorithm is a technique in mathematical programming for complex mixed-integer linear programming (MILP) problems with a particular block structure. The strategy of Benders' decomposition can be described as a strategy of divide and conquer. The Benders' dec...

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Veröffentlicht in:IEEE Wireless Communications and Networking Conference : [proceedings] : WCNC S. 2536 - 2540
Hauptverfasser: Zhao, Zhongqi, Fan, Lei, Han, Zhu
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 10.04.2022
Schlagworte:
Annealing
Benders' Decomposition
Communication
Computational modeling
Computers
Conferences
Hybrid Quantum-Classical Computing
Machine learning
Machine learning algorithms
Mixed-integer Linear Programming
Networking
Quantum annealing
Quantum Computing
ISSN:1558-2612
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Zusammenfassung:The Benders' decomposition algorithm is a technique in mathematical programming for complex mixed-integer linear programming (MILP) problems with a particular block structure. The strategy of Benders' decomposition can be described as a strategy of divide and conquer. The Benders' decomposition algorithm has been employed in a variety of applications such as communication, networking, and machine learning. However, the master problem in Benders' decomposition is still NP-hard, which motivates us to employ quantum computing. In the paper, we propose a hybrid quantum-classical Benders' decomposition algorithm. We transfer the Benders' decomposition's master problem into the quadratic unconstrained binary optimization (QUBO) model and solve it by the state-of-the-art quantum annealer. Then, we analyze the computational results and discuss the feasibility of the proposed algorithm. Due to our reformulation in the master problem in Benders' decomposition, our hybrid algorithm, which takes advantage of both classical and quantum computers, can guarantee the solution quality for solving MILP problems.
ISSN:1558-2612
DOI:10.1109/WCNC51071.2022.9771632