Hilbert–Schmidt Bound On Operator Application
ProvedFunctionalInequalities.Analysis.norm_apply_sq_le_hsNormSq_mul_readablefunctional-inequalitieskls-conjecturekls-modularkls-readablelog-concave
This statement bounds the norm of an operator applied to a vector.
Let n be a natural number, let M be a continuous linear endomorphism of , and let x be a vector. Then
where hsNormSq M is the squared Hilbert–Schmidt norm.
This is a standard finite-dimensional estimate useful for Lyapunov and cutoff bounds.
Formalization Note The statement uses the canonical Euclidean basis and includes all natural numbers n.
Original Lean declaration header
Within namespace FunctionalInequalities.Analysis. Shown in its original module context. Proof omitted.
theorem norm_apply_sq_le_hsNormSq_mul (M : EuclideanSpace ℝ (Fin n) →L[ℝ] EuclideanSpace ℝ (Fin n))
(x : EuclideanSpace ℝ (Fin n)) : ‖M x‖ ^ 2 ≤ hsNormSq M * ‖x‖ ^ 2
Module navigation
Module: Analysis/Operator/HilbertSchmidt.
Next: opNorm_sq_le_hsNormSq.
Source excerpt, lines 76–77 · Frozen source 408915f.
Preamble
import Definitions.Def_KLSModular_FunctionalInequalities_Analysis_Operator_HilbertSchmidt
variable {n : ℕ}
Formal statement
theorem FunctionalInequalities.Analysis.norm_apply_sq_le_hsNormSq_mul_readable (M : EuclideanSpace ℝ (Fin n) →L[ℝ] EuclideanSpace ℝ (Fin n))
(x : EuclideanSpace ℝ (Fin n)) : ‖M x‖ ^ 2 ≤ hsNormSq M * ‖x‖ ^ 2 := by sorry
Source
Lean source: https://github.com/human-ai-mathematics/kls-lean/blob/408915f/formalization/FunctionalInequalities/Analysis/Operator/HilbertSchmidt.lean#L75-L103 (repository human-ai-mathematics/kls-lean, commit 408915f, module `FunctionalInequalities.Analysis.Operator.HilbertSchmidt`, declaration `FunctionalInequalities.Analysis.norm_apply_sq_le_hsNormSq_mul`; the repository is private at upload time).