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On C\mathbb CC, R(Δ)≤4Ψ(M‾)∥Δ∥\mathcal R(\Delta)\le4\Psi(\overline{\mathcal M})\|\Delta\|R(Δ)≤4Ψ(M)∥Δ∥ when θ∗∈M\theta^*\in\mathcal Mθ∗∈M (Section 2.4, p. 10)

Proved
UnifiedMEstimator.General.section24_regularizer_bound_on_C

by mikedeng1 · Sep 27, 2026 · Mathlib 0df444a (Lean v4.33.1)

high-dimensional-statisticsm-estimatorp2o-batch-p100bp2o-gran-per-chapterp2o-plan-paperp2o-v1

Let EEE be a finite-dimensional real inner product space, R\mathcal RR a norm on EEE, and M⊆M‾\mathcal M\subseteq\overline{\mathcal M}M⊆M subspaces of EEE. Write ΔS\Delta_SΔS​ for the orthogonal projection of Δ\DeltaΔ onto a subspace SSS, Ψ\PsiΨ for the subspace compatibility constant and C(M,M‾⊥;θ∗)\mathbb C(\mathcal M,\overline{\mathcal M}^\perp;\theta^*)C(M,M⊥;θ∗) for the set of Eq. (17).

Suppose that θ∗∈M\theta^*\in\mathcal Mθ∗∈M and Δ∈C(M,M‾⊥;θ∗)\Delta\in\mathbb C(\mathcal M,\overline{\mathcal M}^\perp;\theta^*)Δ∈C(M,M⊥;θ∗). Then R(ΔM‾⊥)≤3 R(ΔM‾)\mathcal R(\Delta_{\overline{\mathcal M}^\perp})\le3\,\mathcal R(\Delta_{\overline{\mathcal M}})R(ΔM⊥​)≤3R(ΔM​), and

R(Δ)≤R(ΔM‾⊥)+R(ΔM‾)≤4 R(ΔM‾)≤4 Ψ(M‾) ∥Δ∥.\mathcal R(\Delta)\le\mathcal R(\Delta_{\overline{\mathcal M}^\perp})+\mathcal R(\Delta_{\overline{\mathcal M}})\le4\,\mathcal R(\Delta_{\overline{\mathcal M}})\le4\,\Psi(\overline{\mathcal M})\,\|\Delta\| .R(Δ)≤R(ΔM⊥​)+R(ΔM​)≤4R(ΔM​)≤4Ψ(M)∥Δ∥.

This is the step at which the compatibility constant Ψ(M‾)\Psi(\overline{\mathcal M})Ψ(M) enters the analysis: on the set C\mathbb CC the regularizer is controlled by the error norm. It is used to derive restricted strong convexity from bounds of the form (20), and the same comparison appears in the error bound of Theorem 1.

Formalization Note The paper's display is a chain; each link is a separate conjunct. The standing assumption M⊆M‾\mathcal M\subseteq\overline{\mathcal M}M⊆M of Section 2.2 is a hypothesis; decomposability is not needed for this step and is not assumed.

Preamble
import Mathlib
import Definitions.Def_UnifiedMEstimator_General_Core
Formal statement
namespace UnifiedMEstimator.General

/-- Section 2.4, p. 10, first display: if `θ* ∈ M` (with `M ⊆ M̄`), then every `Δ` in
`C(M, M̄⊥; θ*)` satisfies `R(Δ_{M̄⊥}) ≤ 3R(Δ_{M̄})` and
`R(Δ) ≤ R(Δ_{M̄⊥}) + R(Δ_{M̄}) ≤ 4R(Δ_{M̄}) ≤ 4Ψ(M̄)‖Δ‖`. -/
theorem section24_regularizer_bound_on_C
    {E : Type*} [NormedAddCommGroup E] [InnerProductSpace ℝ E] [FiniteDimensional ℝ E]
    (R : E → ℝ) (M Mbar : Submodule ℝ E) (θstar Δ : E)
    (hR : IsNormFn R) (hle : M ≤ Mbar) (hθ : θstar ∈ M)
    (hΔ : Δ ∈ setC R M Mbar θstar) :
    R (Mbarᗮ.starProjection Δ) ≤ 3 * R (Mbar.starProjection Δ) ∧
    R Δ ≤ R (Mbarᗮ.starProjection Δ) + R (Mbar.starProjection Δ) ∧
    R (Mbarᗮ.starProjection Δ) + R (Mbar.starProjection Δ) ≤ 4 * R (Mbar.starProjection Δ) ∧
    4 * R (Mbar.starProjection Δ) ≤ 4 * compat R Mbar * ‖Δ‖ := by sorry

end UnifiedMEstimator.General
Source
Negahban, Ravikumar, Wainwright and Yu, A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers, arXiv:1010.2731v3, p. 10, Section 2.4, first display
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What the Lean code literally says, in plain math · claude-opus-5-5

Setting. EEE is any finite-dimensional real inner product space with inner product ⟨⋅,⋅⟩\langle\cdot,\cdot\rangle⟨⋅,⋅⟩ and induced norm ∥⋅∥\|\cdot\|∥⋅∥. Write PSP_SPS​ for orthogonal projection onto a subspace SSS and S⊥S^\perpS⊥ for its orthogonal complement. The theorem takes:

  • a function R:E→RR : E \to \mathbb{R}R:E→R;
  • linear subspaces MMM and Mˉ\bar MMˉ of EEE;
  • vectors θ∗\theta^*θ∗ and Δ\DeltaΔ in EEE.

Hypotheses.

  1. RRR is a norm: it is nonnegative, R(x)=0R(x) = 0R(x)=0 exactly when x=0x = 0x=0, R(cx)=∣c∣R(x)R(cx) = |c|R(x)R(cx)=∣c∣R(x), and R(x+y)≤R(x)+R(y)R(x+y) \le R(x) + R(y)R(x+y)≤R(x)+R(y).
  2. M⊆MˉM \subseteq \bar MM⊆Mˉ.
  3. θ∗∈M\theta^* \in Mθ∗∈M.
  4. Δ\DeltaΔ satisfies
R(PMˉ⊥Δ)≤3R(PMˉΔ)+4R(PM⊥θ∗).R(P_{\bar M^\perp}\Delta) \le 3R(P_{\bar M}\Delta) + 4R(P_{M^\perp}\theta^*).R(PMˉ⊥​Δ)≤3R(PMˉ​Δ)+4R(PM⊥​θ∗).

No decomposability of RRR is assumed.

Conclusion. All four of the following hold:

R(PMˉ⊥Δ)≤3R(PMˉΔ),R(P_{\bar M^\perp}\Delta) \le 3R(P_{\bar M}\Delta),R(PMˉ⊥​Δ)≤3R(PMˉ​Δ), R(Δ)≤R(PMˉ⊥Δ)+R(PMˉΔ),R(\Delta) \le R(P_{\bar M^\perp}\Delta) + R(P_{\bar M}\Delta),R(Δ)≤R(PMˉ⊥​Δ)+R(PMˉ​Δ), R(PMˉ⊥Δ)+R(PMˉΔ)≤4R(PMˉΔ),R(P_{\bar M^\perp}\Delta) + R(P_{\bar M}\Delta) \le 4R(P_{\bar M}\Delta),R(PMˉ⊥​Δ)+R(PMˉ​Δ)≤4R(PMˉ​Δ), 4R(PMˉΔ)≤4 Ψ(Mˉ) ∥Δ∥.4R(P_{\bar M}\Delta) \le 4\,\Psi(\bar M)\,\|\Delta\|.4R(PMˉ​Δ)≤4Ψ(Mˉ)∥Δ∥.

Here

Ψ(Mˉ)=sup⁡{R(u)/∥u∥:u∈Mˉ, u≠0},\Psi(\bar M) = \sup\{R(u)/\|u\| : u \in \bar M,\ u \ne 0\},Ψ(Mˉ)=sup{R(u)/∥u∥:u∈Mˉ, u=0},

where the supremum is 000 if the set is empty or unbounded above.

Degenerate cases.

  • Mˉ={0}\bar M = \{0\}Mˉ={0}. Then M={0}M = \{0\}M={0} and θ∗=0\theta^* = 0θ∗=0. Hypothesis 4 reads R(Δ)≤0R(\Delta) \le 0R(Δ)≤0, which forces Δ=0\Delta = 0Δ=0, and every quantity in the conclusion is 000. Since Ψ({0})=0\Psi(\{0\}) = 0Ψ({0})=0 by the empty-set convention, the last inequality reads 0≤00 \le 00≤0.
  • E={0}E = \{0\}E={0}. Everything is 000.
  • Mˉ=E\bar M = EMˉ=E. Then PMˉ⊥Δ=0P_{\bar M^\perp}\Delta = 0PMˉ⊥​Δ=0 and hypothesis 4 holds for every Δ\DeltaΔ.
Human review
  • Endorsed by Shuze Chen · Sep 27, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Sep 27, 2026

    Confirmed by the mission captain (proposal self-audit).

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