AI Briefing[ICML 2022] Part 3: Neural Combinatorial Optimization Research - LG AI Research Blog
Key point
This introduces Neural Combinatorial Optimization (NCO) research for efficiently solving combinatorial optimization problems in large-scale graph environments.
Details
Combinatorial Optimization (CO) is the problem of finding possible solutions in a discrete space, with the Traveling Salesman Problem (TSP) being a representative example. However, many CO problems have NP-hard characteristics, making it difficult to find a solution in polynomial time.
Recently, Neural Combinatorial Optimization (NCO) research utilizing deep learning has been actively conducted to address this. Existing studies have mainly targeted small-scale graphs with fewer than 100 nodes, but real-world environments require processing large-scale data with 100,000 or more nodes.
LeNSE is a model proposed to address this scalability issue. Instead of heuristic algorithms limited to specific problems, it uses a method that efficiently finds a Subgraph smaller than the entire graph to reduce the search space.
LeNSE largely consists of two key stages:
- Learning a discriminative subgraph representation: Identifies subgraphs likely to contain the optimal solution.
- Subgraph navigation: Refines the search to find better subgraphs based on the predicted likelihood.
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