AI Briefing[ICML 2024] Solving Combinatorial Optimization Problems Using Diffusion Models - LG AI Research BLOG
Key point
LG AI Research presented CADO, a study combining diffusion models with reinforcement learning to solve combinatorial optimization problems, at ICML 2024.
Details
Combinatorial Optimization is a field that finds the optimal target value from a finite set of discrete solutions, and it includes challenging problems such as the Traveling Salesman Problem (TSP) and printed circuit board (PCB) design. Most of these problems belong to NP-hard, characterized by computational complexity that grows exponentially.
Previously, manually designed Heuristic algorithms or Neural Network methods that learn data patterns were mainly used. LG AI Research currently utilizes its self-developed heuristic algorithm, Pathfinder++, for automatic PCB routing.
At this year's ICML 2024, LG AI Research's DI Lab presented CADO, a study that combines a Diffusion Model with Reinforcement Learning to solve combinatorial optimization problems. This goes beyond the limitations of existing heuristic methods and presents the latest research direction that incorporates the capabilities of generative models into optimization algorithms.
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