RadonBench: A Benchmark of Classical and Learning-Guided Search Techniques for Problems in Combinatorial Geometry
Abstract
Classical optimization and AI-based search offer complementary approaches to open problems in discrete mathematics, but a systematic comparison of these approaches on a common task is lacking. We benchmark five representative classical and AI-based methods on the integer Radon problem, which asks for large sets of lattice points that cannot be partitioned into two nonempty parts whose convex hulls share a lattice point. The Radon number is the smallest such that every -point subset of admits such a partition. The current bounds are and . Finding a Radon-independent set of 11 points in or 21 points in would raise the corresponding lower bound to 12 or 22. We propose RadonBench, which includes exhaustive extension, greedy local search, and task-specific adaptations of PatternBoost, AlphaZero, and AlphaEvolve-style program evolution. The three AI approaches respectively learn point proposals, guide Monte Carlo tree search with a set-attention policy and value network, and evolve candidate-generating programs. To compare their different output types, we report target recovery, the largest valid construction, and the number of shared Radon points in target-sized near misses, where zero certifies a valid construction. PatternBoost and AlphaZero-Radon construct Radon-independent sets with 10 points in and 20 points in , matching the current best-known sizes. At the open targets, AlphaZero-Radon and exhaustive extension reach a minimum of two shared Radon points, while greedy local search and AlphaEvolve-style program evolution reach one. None of the five methods finds a target-sized Radon-independent set. Thus, whether or remains open.
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