Fitting an ellipsoid to random points: predictions using the replica method
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Abstract (arXiv)
We consider the problem of fitting a centered ellipsoid to n standard Gaussian random vectors in ℝd, as n, d → ∞ with n/d2 → α > 0. It has been conjectured that this problem is, with high probability, satisfiable (SAT; that is, there exists an ellipsoid passing through all n points) for α < 1/4, and unsatisfiable (UNSAT) for α > 1/4. In this work we give a precise analytical argument, based on the non-rigorous replica method of statistical physics, that indeed predicts a SAT/UNSAT transition at α = 1/4, as well as the shape of a typical fitting ellipsoid in the SAT phase (i.e., the lengths of its principal axes). Besides the replica method, our main tool is the dilute limit of extensive-rank "HCIZ integrals" of random matrix theory. We further study different explicit algorithmic constructions of the matrix characterizing the ellipsoid. In particular, we show that a procedure based on minimizing its nuclear norm yields a solution in the whole SAT phase. Finally, we characterize the SAT/UNSAT transition for ellipsoid fitting of a large class of rotationally-invariant random vectors. Our work suggests mathematically rigorous ways to analyze fitting ellipsoids to random vectors, which is the topic of a companion work.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Oct 2023.
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Citing papers per year: 2024: 1; 2025: 4; 2026: 2.
7 papers.
- 2024Bayes-optimal learning of an extensive-width neural network from quadratically many samplescited by 23
- 2025Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regimecited by 18
- 2025The Nuclear Route: Sharp Asymptotics of ERM in Overparameterized Quadratic Networkscited by 11
- 2025Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Lawscited by 5
- 2025Bayes optimal learning of attention-indexed modelscited by 3
- 2026Factual recall in linear associative memories: sharp asymptotics and mechanistic insightscited by 1
- 2026The sharp SAT/UNSAT phase transition in random ellipsoid fittingcited by 0
- 2026The sharp SAT/UNSAT phase transition in random ellipsoid fitting10 Aug 2026
- 2026Factual recall in linear associative memories: sharp asymptotics and mechanistic insights11 May 2026
- 2025Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws29 Sep 2025
- 2025Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime29 Sep 2025
- 2025Bayes optimal learning of attention-indexed models2 Jun 2025
- 2025The Nuclear Route: Sharp Asymptotics of ERM in Overparameterized Quadratic Networks23 May 2025
- 2024Bayes-optimal learning of an extensive-width neural network from quadratically many samples7 Aug 2024