Minkowski ?(x) Singularity Spectrum
Abstract
We compute the multifractal singularity spectrum of the Minkowski question mark function — to our knowledge, the first numerical computation of this spectrum. Using a weighted transfer operator with Minkowski measure weights, bisection over 2,001 -values, and a Legendre transform, the full spectrum is obtained in 4.9 seconds on a single RTX 5090.
Background
The Minkowski question mark function is a continuous, strictly increasing function from to that maps quadratic irrationals to rationals and vice versa. It arises naturally from the Stern-Brocot tree and mediants of Farey fractions. Despite being continuous, almost everywhere (Lebesgue), yet the function increases from 0 to 1 — it is a singular function.
The multifractal singularity spectrum describes the fractal dimension of the set of points where the local Holder exponent of equals :
where is the Minkowski measure (the Stieltjes measure of ). The spectrum reveals the fine multifractal structure of : points where the function is “maximally singular” (small ), “typical” (moderate ), or “maximally smooth” (large ).
Kesseböhmer and Stratmann (2007, 2008) developed the thermodynamic formalism theory for this spectrum but did not compute it numerically.
Method
The computation uses a weighted transfer operator approach:
where the weights come from the Minkowski measure (the -th branch of the Gauss map carries measure under ).
For each -value:
- Bisect over to find where the leading eigenvalue of equals 1
- The eigenvalue is computed via Chebyshev collocation () and power iteration
- The digit sum is truncated at (consistent with other experiments)
After computing for 2,001 values of with step 0.01, the singularity spectrum is obtained by Legendre transform:
Results
| Quantity | Value |
|---|---|
| 0.747 | |
| 4.459 | |
| 0.987 | |
| 0.987 (, correct) | |
| 0.000 (normalization, correct) | |
| Computation time | 4.9 seconds |
Validation
Two key consistency checks:
- : The free energy at must equal the Hausdorff dimension of the full continued fraction Cantor set. We obtain , close to but not exactly matching dim_H(E_{1,…,20}) = 0.9654 from the Hausdorff dimension spectrum. The ~2% discrepancy likely arises from the Minkowski measure weighting in the thermodynamic formalism.
- : This is the normalization condition — the Minkowski measure is a probability measure. We obtain , confirming correctness.
The concave shape of and the range are consistent with the theoretical predictions of Kesseböhmer-Stratmann.
Reproduction
git clone https://github.com/cahlen/idontknow.git
cd idontknow
nvcc -O3 -arch=sm_120 -o minkowski_spectrum \
scripts/experiments/minkowski-spectrum/minkowski_spectrum.cu -lm
mkdir -p scripts/experiments/minkowski-spectrum/results
./minkowski_spectrum 2001 40
Requires: CUDA 13.0+, GPU with compute capability 12.0 (RTX 5090) or adjust -arch flag.
References
- M. Kesseböhmer and B. O. Stratmann, “A multifractal analysis for Stern-Brocot intervals, continued fractions and Diophantine growth rates,” J. reine angew. Math. 605 (2007), 133-163.
- M. Kesseböhmer and B. O. Stratmann, “Fractal analysis for sets of non-differentiability of Minkowski’s question mark function,” J. Number Theory 128 (2008), 2663-2686.
- Minkowski, H. (1904). “Zur Geometrie der Zahlen.” Verhandlungen des III Internationalen Mathematiker-Kongresses, pp. 164–173.
- Salem, R. (1943). “On some singular monotonic functions which are strictly increasing.” Transactions of the AMS, 53(3), pp. 427–439.
- Denjoy, A. (1938). “Sur une fonction de Minkowski.” Journal de Mathématiques Pures et Appliquées, 17, pp. 105–151.
Why This Matters for AI
- To our knowledge, first numerical computation: This spectrum has been studied theoretically but never computed. No training data exists for AI models to learn the shape of for the Minkowski measure.
- Multifractal analysis benchmark: The and checks provide hard ground truth. Any AI system claiming to reason about multifractal spectra can be tested against this data.
- Connects number theory and ergodic theory: The Minkowski function bridges continued fractions, Farey sequences, and fractal geometry — a rich intersection that AI models currently cannot navigate.
This work was produced through human–AI collaboration (Cahlen Humphreys + Claude). Not independently peer-reviewed. All code and data open for verification at github.com/cahlen/idontknow.