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CSCE 52803 — Graph and Combinatorial Algorithms

3 credits · 3 hours

Advanced topics in graph and combinatorial optimization problems. Traditional approach section: P and NP problems, proof of NP-completeness, approximation algorithms for solving NP hard problems. Machine learning-based approach section: graph neural networks, deep reinforcement learning, state-of-the-art machine learning approaches for solving graph and combinatorial optimization problems. Prerequisite: Graduate standing in CSCE Department. (Typically offered: Irregular)

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