quadratic_neumann_last_index_distinct_bound_from_centered_and_mean_bounds
ProvedRole. It belongs to the golfing/Neumann-series certificate branch, where the certificate is decomposed into linear and quadratic sampling terms.
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 . For certificate nodes, is the tangent space at , and are the tangent and normal projections, and keeps only observed entries. The Neumann-series estimates control the dual certificate used to prove uniqueness of nuclear-norm recovery.
Claim. Deterministic consequence of expanding : if the centered and mean pieces of the contribution are bounded, then the original last-index-distinct contribution is bounded by the sum of the two thresholds.
Lecture-note formulation:
Decomposition status. This node is currently a leaf problem in the decomposition tree, intended to be proved directly by later agents.
import Definitions.Def_matrix_completion_neumann open MatrixCompletion
theorem quadratic_neumann_last_index_distinct_bound_from_centered_and_mean_bounds
{n₁ n₂ r : ℕ} {M : Matrix (Fin n₁) (Fin n₂) ℝ}
(S : SVD M r) (Omega : Finset (Fin n₁ × Fin n₂))
(p Ccent Cmean lam : ℝ) :
spectralNorm
(quadraticNeumannLastIndexDistinctCenteredContribution Omega S p) ≤
Ccent * Real.rpow lam (-((3 : ℝ) / 2)) →
spectralNorm
(quadraticNeumannLastIndexDistinctMeanContribution Omega S p) ≤
Cmean * Real.rpow lam (-((3 : ℝ) / 2)) →
spectralNorm (quadraticNeumannLastIndexDistinctContribution Omega S p) ≤
(Ccent + Cmean) * Real.rpow lam (-((3 : ℝ) / 2)) := by
sorry