Charton, F., Ellenberg, J., Wagner, A. Z., & Williamson, G. (2024). PatternBoost: Constructions in Mathematics with a Little Help from AI. arXiv preprint arXiv:2411.00566.
This paper introduces PatternBoost, a new computational method for generating interesting constructions in pure mathematics, particularly in extremal combinatorics. The authors aim to demonstrate the effectiveness of this method in solving problems where optimal constructions are difficult to describe or discover using traditional approaches.
PatternBoost employs an iterative process alternating between "local" and "global" phases. The local phase utilizes a simple greedy algorithm for local search, while the global phase leverages a transformer neural network trained on the best constructions found so far. This iterative process allows the algorithm to learn from previous successful constructions and generate new ones with improved properties. The authors apply PatternBoost to several problems in extremal combinatorics, including finding triangle-free graphs with many edges, constructing binary matrices with large permanents avoiding specific patterns, and finding spanning subgraphs of hypercubes with minimal edges while maintaining diameter.
The authors demonstrate that PatternBoost outperforms pure local search methods in various extremal combinatorial problems. In some cases, it even approaches the performance of handcrafted, problem-specific algorithms. For instance, PatternBoost discovers a counterexample to a 30-year-old conjecture about the minimum number of edges in a spanning subgraph of a hypercube with the same diameter.
PatternBoost presents a promising approach for exploring complex mathematical constructions, particularly in extremal combinatorics. Its ability to learn from data and generate novel solutions makes it a valuable tool for mathematicians seeking to discover new patterns and solve challenging problems.
This research highlights the potential of machine learning, specifically transformer networks, in advancing mathematical research. It provides a practical and accessible method for mathematicians to leverage the power of AI in their work.
While PatternBoost shows significant promise, the authors acknowledge that its performance varies across different problems. Further research is needed to understand which mathematical problems are most amenable to this approach and to explore the impact of different tokenization schemes and transformer architectures on the algorithm's effectiveness. Additionally, investigating the potential of PatternBoost beyond extremal combinatorics and in other scientific domains could lead to further breakthroughs.
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