Reinforcement Learning-Based Formulations With Hamiltonian-Inspired Loss Functions for Combinatorial Optimization Over Graphs

Reinforcement Learning-Based Formulations With Hamiltonian-Inspired Loss Functions for Combinatorial Optimization Over Graphs

Quadratic Unconstrained Binary Optimization (QUBO) is a versatile approach used to represent a wide range of NP-hard Combinatorial Optimization (CO) problems through binary variables. The transformation of QUBO to an Ising Hamiltonian is recognized as an effective method for solving key optimization...

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Bibliographic Details
Main Authors: Redwan Ahmed Rizvee, Raheeb Hassan, Md. Mosaddek Khan
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deep reinforcement learning
graph neural network
Hamiltonian function
Monte Carlo tree search
Online Access:https://ieeexplore.ieee.org/document/10752916/
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