Theoretical computer science·OpenAI Math·Result 117·Lean formalisation listed
Uniform sparsest cut: hardness and semidefinite gaps
One of the 372 results in OpenAI's public mathematics catalogue, filed under theoretical computer science: 2 preprints, the latest dated 24 September 2026. This page quotes OpenAI's own summary of the claim, links the papers and abstracts, and will embed the animation.
Latest paper
Constant-factor hardness of uniform sparsest cut
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What OpenAI claims
Proves that approximating Uniform Sparsest Cut within any fixed constant factor is NP-hard, even with nonnegative rational capacities and unit demands. The Goemans–Linial semidefinite relaxation also has integrality gaps of order at least √(log n)/(log log n)3, approaching the square-root-logarithmic upper bound. (Lean)
Papers (2)
Constant-factor hardness of uniform sparsest cut
We prove that, for every fixed C > 1, approximating Uniform Sparsest Cut within factor C is NP-hard. The output graphs have nonnegative rational capacities and unit demand between every pair of distinct vertices.
Near-square-root logarithmic integrality gaps for uniform sparsest cut
We construct uniform sparsest-cut instances whose Goemans–Linial semidefinite integrality gap is at least c√(log n)/(log log n)3 along a sequence n → ∞. The demand is one between every pair of distinct vertices, and the capacities are nonnegative real numbers. This matches the Arora–Rao–Vazirani upper bound up to a power of log log n.
Status
Preprint. The papers have not been independently reviewed. Treat the result as OpenAI's claim until other mathematicians have checked it. If a video exists, it illustrates the statement; it is not the proof.
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