Gotsman--Linial: GradingShift #
Adapted from OpenAI's formalization of Average Sensitivity of Polynomial Threshold
Functions (25 September 2026), at revision adc7f1241b42e322a6451854ab7e4b4c146bf78a.
Source: https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/lean/OAI/Combinatorics/GotsmanLinial/GradingShift.lean
The original Boolean-cube representation is used internally in this proof development;
Complexitylib.BooleanAnalysis.PolynomialThreshold transports the result to the
library's Fourier and influence API. Changes here adapt module visibility,
namespace names, formatting, and redundant simp attributes to complexitylib.
A degree shift is tridiagonal in the associated orthogonal grading #
These lemmas retain the degree-shift premise explicitly. The cube application discharges it using coordinate multiplication and the weighted Fourier flag.
An operator that raises the flag index by at most one has no blocks strictly above the first off-diagonal.
Self-adjointness gives the other half of the band by taking adjoints.
The band property is preserved by taking matrices or any other ring representation of the operator algebra.
Commutation transports a degree-shift statement through a weight map.
The concrete matrix form consumed by the commutator estimate.
Coordinate signs commute with the actual positive square-root weight.
The actual weighted Fourier flag retains the one-step coordinate shift.
Tridiagonality for the actually constructed weighted Fourier projections, expressed in an arbitrary orthonormal coordinate system.
The raw coordinate-sign matrices are tridiagonal for the actual weighted Fourier projection family. There is no remaining degree-shift hypothesis.