fixed_matrix_centered_sampling_log_moment_bound
ProvedRole. It is a centered-sampling fluctuation estimate, one of the reusable concentration interfaces used repeatedly by the Neumann-term bounds.
Problem and notation. Exact matrix completion asks when an unknown low-rank real matrix can be recovered from a random subset of its entries. Here has rank , entries are observed, and . Recovery means nuclear-norm minimization: minimize among matrices agreeing with on the observed entries. Probability notation. is the fixed-cardinality success probability: is chosen uniformly among all subsets of entries with , and the event is that the convex program uniquely returns . In Bernoulli nodes, or means each entry is sampled independently with probability , usually . Coherence notation. The object records SVD/singular-vector data for . The hypotheses and are the Candes-Recht incoherence assumptions: measures how spread out the singular vector spaces are, and measures the largest entry of the sign matrix . The parameter controls polynomial failure probabilities such as .
Claim. Log-moment estimate behind Candes-Recht Theorem 6.3: for comparable to , the th moment of the centered sampling fluctuation is bounded at the natural scale. This is the symmetrization plus noncommutative-Khintchine part of Section 6.1.
Lecture-note formulation:
The constants in this node are universal existential constants; the theorem asserts that some positive constants with these roles exist.
Decomposition status. A corresponding proof sketch reduces this node to smaller mathematical subclaims. The checked reduction uses 2 subclaims: Bernoulli sampled row column energy controlled log moment bound; centered sampling log moment from row column energy.
import Definitions.Def_matrix_completion_neumann open MatrixCompletion
theorem fixed_matrix_centered_sampling_log_moment_bound :
∃ C : ℝ, 0 < C ∧
∀ (β : ℝ), 2 < β →
∀ (n₁ n₂ m : ℕ) (X : Matrix (Fin n₁) (Fin n₂) ℝ),
0 < n₁ → 0 < n₂ → m ≤ n₁ * n₂ →
(m : ℝ) ≥ β * (↑(max n₁ n₂)) *
Real.log (↑(max n₁ n₂)) →
∃ q : ℕ, 1 ≤ q ∧
(q : ℝ) ≥ β * Real.log (↑(max n₁ n₂)) ∧
bernoulliExpectation ((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))
(fun Omega =>
spectralNorm
(centeredSamplingFluctuation Omega
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ))) X) ^ q) ≤
(C * Real.sqrt
((β * (↑(max n₁ n₂)) *
Real.log (↑(max n₁ n₂))) /
((m : ℝ) / ((n₁ : ℝ) * (n₂ : ℝ)))) *
entrySupNorm X) ^ q := by
sorry