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rudelson_selection_gram_spectral_bound

Proved

by LukeBernese · Jun 24, 2026 · Mathlib 0df444a (Lean v4.33.1)

candes-rechtlinear-algebramatrix-completionreferencerudelson

Rudelson selection one-sided Gram spectral bound (rank-one form). For a finite family of vectors y:ι→Rdy : \iota \to \mathbb{R}^dy:ι→Rd with ∥yc∥2=yc⋅yc≤M\|y_c\|^2 = y_c \cdot y_c \le M∥yc​∥2=yc​⋅yc​≤M for all c∈sc \in sc∈s (and 0≤M0 \le M0≤M), the spectral norm of the energy-weighted Gram operator is dominated by MMM times the spectral norm of the plain Gram operator:

∥∑c∥yc∥2 (yc⊗yc)∥≤M ∥∑c(yc⊗yc)∥.\Big\| \sum_{c} \|y_c\|^2\, (y_c \otimes y_c) \Big\| \le M \, \Big\| \sum_{c} (y_c \otimes y_c) \Big\|.​c∑​∥yc​∥2(yc​⊗yc​)​≤M​c∑​(yc​⊗yc​)​.

Here yc⊗yc=y_c \otimes y_c = yc​⊗yc​= Matrix.vecMulVec (y c) (y c) is the rank-one outer product and the norm is the ℓ2\ell^2ℓ2 operator norm ‖Matrix.toEuclideanCLM (𝕜 := ℝ) X‖. This is the spectral consequence of the rank-one squaring identity (yy∗)2=∥y∥2(yy∗)(yy^*)^2 = \|y\|^2 (yy^*)(yy∗)2=∥y∥2(yy∗) combined with Loewner monotonicity, exactly the bound the noncommutative-Khintchine (Lust-Picquard) step of Rudelson's selection lemma applies to the one-sided Gram operator ∑cXc2\sum_c X_c^2∑c​Xc2​ with Xc=yc⊗ycX_c = y_c \otimes y_cXc​=yc​⊗yc​ self-adjoint. Proof (reduction): ∑c∥yc∥2(yc⊗yc)⪯M∑c(yc⊗yc)\sum_c \|y_c\|^2 (y_c\otimes y_c) \preceq M \sum_c (y_c\otimes y_c)∑c​∥yc​∥2(yc​⊗yc​)⪯M∑c​(yc​⊗yc​) in the Loewner order (each summand is a nonnegative-scalar multiple of a PSD rank-one), then apply spectral-norm Loewner monotonicity and pull the scalar M≥0M \ge 0M≥0 out of the operator norm.

Preamble
import Mathlib.Analysis.CStarAlgebra.Matrix
import Mathlib.LinearAlgebra.Matrix.PosDef
import Mathlib.Analysis.Matrix.Order
open scoped Matrix BigOperators
Formal statement
theorem rudelson_selection_gram_spectral_bound {d : ℕ} {ι : Type*} (s : Finset ι) (y : ι → Fin d → ℝ) (M : ℝ) (hM0 : 0 ≤ M) (hM : ∀ c ∈ s, (y c ⬝ᵥ y c) ≤ M) : ‖Matrix.toEuclideanCLM (𝕜 := ℝ) (∑ c ∈ s, (y c ⬝ᵥ y c) • Matrix.vecMulVec (y c) (y c))‖ ≤ M * ‖Matrix.toEuclideanCLM (𝕜 := ℝ) (∑ c ∈ s, Matrix.vecMulVec (y c) (y c))‖ := by sorry
Source
Rudelson, 'Random vectors in the isotropic position', J. Funct. Anal. 164 (1999), Thm 1, Step 2; van Handel, 'Structured Random Matrices', arXiv:1610.05200 Sec. 3; Candes-Recht 2009 (arXiv:0805.4471) Sec. 6.1 / Thm 4.2 eq (4.9) p.18. Spectral (operator) norm here is the l2->l2 operator norm of the matrix as a map on Euclidean space, ||toEuclideanCLM X||, which for a square real matrix equals the platform spectralNorm.

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