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Lemma 1.3: an SSS-sparse representation is unique when δ2S<1\delta_{2S} < 1δ2S​<1

Proved
CandesTao.Decoding.sparse_representation_unique

by naimengye · Sep 30, 2026 · Mathlib 0df444a (Lean v4.33.1)

compressed-sensingerror-correcting-codesl1-minimizationlinear-programmingrestricted-isometrysparse-recovery

Let FFF be a real p×mp \times mp×m matrix with columns v1,…,vmv_1, \dots, v_mv1​,…,vm​, and suppose that S≥1S \ge 1S≥1 is such that δ2S<1\delta_{2S} < 1δ2S​<1. Let TTT be a set of at most SSS indices, let ccc be an arbitrary real coefficient vector supported on TTT, and put f:=FTc=∑j∈Tcjvjf := F_T c = \sum_{j \in T} c_j v_jf:=FT​c=∑j∈T​cj​vj​. Then the set TTT and the coefficients (cj)j∈T(c_j)_{j \in T}(cj​)j∈T​ can be reconstructed uniquely from knowledge of fff and the vjv_jvj​: if c′c'c′ is supported on a set T′T'T′ with ∣T′∣≤S|T'| \le S∣T′∣≤S and

∑jcj′vj=f,\sum_{j} c'_j v_j = f ,j∑​cj′​vj​=f,

then c′=cc' = cc′=c.

This is the abstract existence statement behind sparse recovery: under δ2S<1\delta_{2S} < 1δ2S​<1, an SSS-sparse vector is determined by its image FcFcFc. It supplies no efficient algorithm; Theorem 1.4 shows that under the slightly stronger condition (1.10) the linear program (P1)(P_1)(P1​) finds this unique representation.

Formalization Note "The set TTT and the coefficients can be reconstructed uniquely" is formalized as the statement the paper proves: any two representations of fff by coefficient vectors supported on sets of size at most SSS coincide as vectors of Rm\mathbb{R}^mRm, so the support (the set of nonzero coordinates) is determined as well. The hypothesis 2S≤m2S \le m2S≤m is the domain of δ2S\delta_{2S}δ2S​ in Definition 1.1.

Preamble
import Definitions.Def_CandesTao_Decoding_RestrictedIsometry
Formal statement
namespace CandesTao.Decoding
theorem sparse_representation_unique {p m : ℕ} (F : Matrix (Fin p) (Fin m) ℝ) (S : ℕ)
    (hS : 1 ≤ S) (hSm : 2 * S ≤ m) (hδ : restrictedIsometryConst F (2 * S) < 1)
    (T T' : Finset (Fin m)) (hT : T.card ≤ S) (hT' : T'.card ≤ S)
    (c c' : Fin m → ℝ) (hc : SupportedOn c T) (hc' : SupportedOn c' T')
    (hf : F.mulVec c = F.mulVec c') : c = c' := by sorry
end CandesTao.Decoding
Source
Candès--Tao 2005, Decoding by Linear Programming, IEEE Trans. Inform. Theory 51(12):4203-4215, doi:10.1109/TIT.2005.858979; arXiv:math/0502327v1 (https://arxiv.org/abs/math/0502327), p. 5, Lemma 1.3 and its proof
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What the Lean code literally says, in plain math · claude-fable-5-1

Read-back of sparse_representation_unique

Data and notation. Fix natural numbers ppp and mmm (both are arbitrary a priori, including 000; see the edge cases below), a real p×mp \times mp×m matrix F=(Fij)i∈[p], j∈[m]F = (F_{ij})_{i \in [p],\, j \in [m]}F=(Fij​)i∈[p],j∈[m]​, where [k][k][k] denotes the index set {0,1,…,k−1}\{0, 1, \dots, k-1\}{0,1,…,k−1}, and a natural number SSS. Vectors indexed by [m][m][m] are written c=(cj)j∈[m]∈Rmc = (c_j)_{j \in [m]} \in \mathbb{R}^mc=(cj​)j∈[m]​∈Rm; Fc∈RpFc \in \mathbb{R}^pFc∈Rp is the ordinary matrix–vector product, (Fc)i=∑j∈[m]Fij cj(Fc)_i = \sum_{j \in [m]} F_{ij}\, c_j(Fc)i​=∑j∈[m]​Fij​cj​. Throughout, ∥x∥:=∑ixi2\|x\| := \sqrt{\sum_i x_i^2}∥x∥:=∑i​xi2​​ is the Euclidean norm of a real vector (the square root is applied to a non-negative number, so no convention for negative arguments is involved), so that ∥x∥2=∑ixi2\|x\|^2 = \sum_i x_i^2∥x∥2=∑i​xi2​.

Custom notions, unfolded.

  • "ccc is supported on TTT", for c∈Rmc \in \mathbb{R}^mc∈Rm and a finite set T⊆[m]T \subseteq [m]T⊆[m], means: for every index j∈[m]j \in [m]j∈[m] with j∉Tj \notin Tj∈/T, one has cj=0c_j = 0cj​=0. Equivalently, supp⁡(c)⊆T\operatorname{supp}(c) \subseteq Tsupp(c)⊆T. It does not require cj≠0c_j \neq 0cj​=0 for j∈Tj \in Tj∈T; in particular the zero vector is supported on every TTT, including T=∅T = \emptysetT=∅, and the only vector supported on ∅\emptyset∅ is 000.

  • The restricted isometry constant of order kkk of FFF, written here δk(F)\delta_k(F)δk​(F), is the real number

δk(F):=inf⁡{ δ∈R  ∣  δ≥0  and  ∀ T⊆[m] with ∣T∣≤k,  ∀ c∈Rm supported on T:  (1−δ) ∥c∥2≤∥Fc∥2  and  ∥Fc∥2≤(1+δ) ∥c∥2 }.\delta_k(F) := \inf \Bigl\{\, \delta \in \mathbb{R} \;\Big|\; \delta \ge 0 \ \text{ and }\ \forall\, T \subseteq [m] \text{ with } |T| \le k,\ \ \forall\, c \in \mathbb{R}^m \text{ supported on } T:\ \ (1-\delta)\,\|c\|^2 \le \|Fc\|^2 \ \text{ and }\ \|Fc\|^2 \le (1+\delta)\,\|c\|^2 \,\Bigr\}.δk​(F):=inf{δ∈R​δ≥0  and  ∀T⊆[m] with ∣T∣≤k,  ∀c∈Rm supported on T:  (1−δ)∥c∥2≤∥Fc∥2  and  ∥Fc∥2≤(1+δ)∥c∥2}.

All inequalities are non-strict. The quantification runs over every subset TTT of [m][m][m] of cardinality at most kkk (including T=∅T = \emptysetT=∅ and all sizes strictly below kkk) and over every real vector supported on TTT (including c=0c = 0c=0, for which both inequalities read 0≤00 \le 00≤0).

The infimum is Mathlib's infimum on R\mathbb{R}R, which carries the convention that the infimum of the empty set, or of a set that is not bounded below, is 000. For this particular set: it is bounded below by 000 by construction (every member satisfies δ≥0\delta \ge 0δ≥0), and it contains every sufficiently large δ\deltaδ (for δ≥1\delta \ge 1δ≥1 the left inequality holds automatically because its left-hand side is ≤0≤∥Fc∥2\le 0 \le \|Fc\|^2≤0≤∥Fc∥2, and for δ≥∑i,jFij2\delta \ge \sum_{i,j} F_{ij}^2δ≥∑i,j​Fij2​ the right inequality holds for all ccc by Cauchy–Schwarz), so it is non-empty. Hence the 000-convention is not triggered and δk(F)\delta_k(F)δk​(F) is the genuine infimum, a real number ≥0\ge 0≥0. Moreover, since all the defining inequalities are non-strict, the set is closed in R\mathbb{R}R, so the infimum is attained; consequently the condition "δk(F)<1\delta_k(F) < 1δk​(F)<1" says exactly that there is some δ∈[0,1)\delta \in [0,1)δ∈[0,1) such that (1−δ)∥c∥2≤∥Fc∥2≤(1+δ)∥c∥2(1-\delta)\|c\|^2 \le \|Fc\|^2 \le (1+\delta)\|c\|^2(1−δ)∥c∥2≤∥Fc∥2≤(1+δ)∥c∥2 for all TTT with ∣T∣≤k|T| \le k∣T∣≤k and all ccc supported on TTT.

(The preamble also defines an ℓ1\ell^1ℓ1 norm, the columns and column span of FFF, and a restricted orthogonality constant; none of these occur in this theorem.)

The statement. For every p,m,F,Sp, m, F, Sp,m,F,S as above, assume:

  1. 1≤S1 \le S1≤S;
  2. 2S≤m2S \le m2S≤m;
  3. δ2S(F)<1\delta_{2S}(F) < 1δ2S​(F)<1: the restricted isometry constant of FFF of order 2S2S2S, as unfolded above, is strictly less than 111;
  4. T,T′⊆[m]T, T' \subseteq [m]T,T′⊆[m] are finite index sets with ∣T∣≤S|T| \le S∣T∣≤S and ∣T′∣≤S|T'| \le S∣T′∣≤S (no other relation between TTT and T′T'T′ is assumed: they may overlap, coincide, be disjoint, or be empty);
  5. c,c′∈Rmc, c' \in \mathbb{R}^mc,c′∈Rm with ccc supported on TTT and c′c'c′ supported on T′T'T′, i.e. cj=0c_j = 0cj​=0 for all j∉Tj \notin Tj∈/T and cj′=0c'_j = 0cj′​=0 for all j∉T′j \notin T'j∈/T′;
  6. Fc=Fc′Fc = Fc'Fc=Fc′ as vectors in Rp\mathbb{R}^pRp, i.e. ∑jFij cj=∑jFij cj′\sum_{j} F_{ij}\, c_j = \sum_{j} F_{ij}\, c'_j∑j​Fij​cj​=∑j​Fij​cj′​ for every i∈[p]i \in [p]i∈[p].

Then c=c′c = c'c=c′, i.e. cj=cj′c_j = c'_jcj​=cj′​ for every j∈[m]j \in [m]j∈[m].

In compact form: for every real p×mp \times mp×m matrix FFF and every natural number SSS with 1≤S1 \le S1≤S, 2S≤m2S \le m2S≤m and δ2S(F)<1\delta_{2S}(F) < 1δ2S​(F)<1,

∀ T,T′⊆[m] with ∣T∣≤S, ∣T′∣≤S,∀ c,c′∈Rm with supp⁡(c)⊆T, supp⁡(c′)⊆T′:Fc=Fc′  ⟹  c=c′.\forall\, T, T' \subseteq [m] \text{ with } |T| \le S,\ |T'| \le S,\qquad \forall\, c, c' \in \mathbb{R}^m \text{ with } \operatorname{supp}(c) \subseteq T,\ \operatorname{supp}(c') \subseteq T':\qquad Fc = Fc' \;\Longrightarrow\; c = c'.∀T,T′⊆[m] with ∣T∣≤S, ∣T′∣≤S,∀c,c′∈Rm with supp(c)⊆T, supp(c′)⊆T′:Fc=Fc′⟹c=c′.

Edge cases and what the quantifiers silently include.

  • p=0p = 0p=0: then Rp\mathbb{R}^pRp has a single element, FcFcFc is the empty vector and ∥Fc∥2=0\|Fc\|^2 = 0∥Fc∥2=0 for every ccc. Because hypotheses 1–2 give m≥2S≥2m \ge 2S \ge 2m≥2S≥2, the index 000 exists; taking T={0}T = \{0\}T={0} and c=e0c = e_0c=e0​ (the vector with c0=1c_0 = 1c0​=1 and all other entries 000) in the definition of δ2S(F)\delta_{2S}(F)δ2S​(F) forces 1−δ≤01 - \delta \le 01−δ≤0, so the defining set is exactly [1,∞)[1, \infty)[1,∞) and δ2S(F)=1\delta_{2S}(F) = 1δ2S​(F)=1. Hypothesis 3 then fails, and the statement holds vacuously for p=0p = 0p=0.
  • m∈{0,1}m \in \{0, 1\}m∈{0,1} and S=0S = 0S=0 are excluded by hypotheses 1–2 (together they give m≥2m \ge 2m≥2). Otherwise mmm and ppp are unrestricted; in particular ppp may be smaller than, equal to, or larger than mmm.
  • The conclusion is demanded in particular when T=T′T = T'T=T′, when c=c′=0c = c' = 0c=c′=0, and when exactly one of c,c′c, c'c,c′ is the zero vector; hypothesis 5 never forces any entry to be non-zero.
  • Hypothesis 3 concerns supports of cardinality up to 2S2S2S, whereas TTT and T′T'T′ in hypothesis 4 each have cardinality at most SSS. The theorem asserts nothing about pairs of vectors whose supports have more than SSS elements, and nothing about matrices with δ2S(F)≥1\delta_{2S}(F) \ge 1δ2S​(F)≥1.
  • SSS and the cardinality bounds are natural numbers; 2S2S2S is the natural number S+SS + SS+S, with no truncation or rounding involved.
Human review
  • Endorsed by Shuze Chen · Oct 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by naimengye · Oct 1, 2026

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

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