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Chapter 27: the Euclidean norm on ℝ^m, r-covers and the covering number N(r, A) (Definition 27.1)

Definition
UnderstandingML_Covering

by naimengye · Sep 24, 2026 · Mathlib 0df444a (Lean v4.33.1)

covering-numbersmetric-entropy

Chapter 27 of Shalev-Shwartz and Ben-David. eucNorm v =∥v∥2=∑ivi2= \|v\|_2 = \sqrt{\sum_i v_i^2}=∥v∥2​=∑i​vi2​​ on Rm\mathbb{R}^mRm. Definition 27.1 (Covering). IsCover r A A' says that A⊆RmA \subseteq \mathbb{R}^mA⊆Rm is rrr-covered by the finite set A′A'A′ with respect to the Euclidean metric: for all a∈Aa \in Aa∈A there exists a′∈A′a' \in A'a′∈A′ with ∥a−a′∥≤r\|a - a'\| \le r∥a−a′∥≤r. coveringNumber r A is N(r,A)N(r, A)N(r,A), the cardinality of the smallest A′A'A′ that rrr-covers AAA, as an infimum in N∪{∞}\mathbb{N} \cup \{\infty\}N∪{∞} (∞\infty∞ if no finite cover exists).

Definition code
import Definitions.Def_UnderstandingML_Rademacher
import Mathlib.LinearAlgebra.Dimension.Finrank

/-!
# Shalev-Shwartz and Ben-David, *Understanding Machine Learning*, Chapter 27: covering numbers

Shalev-Shwartz and Ben-David, *Understanding Machine Learning: From Theory to Algorithms*,
Cambridge University Press 2014, doi:10.1017/CBO9781107298019, §27.1–§27.2.

**Covering (Definition 27.1, p. 388).** `A ⊆ ℝ^m` is `r`-covered by `A'`, with respect to the
Euclidean metric, if for all `a ∈ A` there is `a' ∈ A'` with `‖a − a'‖ ≤ r`; `N(r, A)` is the
cardinality of the smallest `A'` that `r`-covers `A`.

**Chaining (§27.2, p. 389).** With `c = min_ā max_{a ∈ A} ‖a − ā‖`, Dudley's chaining bounds the
Rademacher complexity by `R(A) ≤ c 2^{−M}/√m + (6c/m) ∑_{k=1}^M 2^{−k} √(log N(c 2^{−k}, A))`.

**Conventions.** Vectors are `Fin m → ℝ` with the explicit Euclidean norm `eucNorm` (Mathlib's
`‖·‖` on this type is the sup norm). A cover is a finset of arbitrary vectors of `ℝ^m`, and
`N(r, A)` is an `ℕ∞`-valued infimum, `⊤` when no finite cover exists; for bounded `A` it is
finite, and the chaining bounds read it through `ENat.toNat`. The chaining lemma is stated for
any enclosing radius `c` about any center `ā`, of which the book's minimal `c` is a special
case; `R(A)` is Chapter 26's `rademacher`.
-/

open MeasureTheory

namespace UnderstandingML

section Covering

variable {m : ℕ}

/-- The Euclidean norm `‖v‖ = √(∑ᵢ vᵢ²)` on `ℝ^m`. -/
noncomputable def eucNorm (v : Fin m → ℝ) : ℝ := Real.sqrt (∑ i, v i ^ 2)

/-- **Definition 27.1.** `A'` is an `r`-cover of `A` with respect to the Euclidean metric: every
`a ∈ A` is within distance `r` of some `a' ∈ A'`. -/
def IsCover (r : ℝ) (A : Set (Fin m → ℝ)) (A' : Finset (Fin m → ℝ)) : Prop :=
  ∀ a ∈ A, ∃ a' ∈ A', eucNorm (a - a') ≤ r

/-- **Definition 27.1.** The **covering number** `N(r, A)`: the cardinality of the smallest finite
`r`-cover of `A` (`⊤` if there is none). -/
noncomputable def coveringNumber (r : ℝ) (A : Set (Fin m → ℝ)) : ℕ∞ :=
  ⨅ (A' : Finset (Fin m → ℝ)) (_ : IsCover r A A'), (A'.card : ℕ∞)

end Covering

end UnderstandingML
Source
Shalev-Shwartz and Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press 2014, doi:10.1017/CBO9781107298019, §27.1 p. 388, Definition 27.1

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