Lemma 2.2: dual sparse reconstruction property, version
ProvedCandesTao.Decoding.dual_reconstruction_linfLet be a real matrix with columns spanning . Let be such that , and let be a real vector supported on with . Then there exists a vector such that for all , and
(equation (2.4)).
Applied to the sign vector of on , this produces the dual vector with on and off whenever ; Theorem 1.4 follows from it by the duality argument of Section 2.2.
Formalization Note The constants are transcribed exactly as printed in (2.4). The mission description ("Difficulty") records a caveat about how the paper's proof arrives at these constants. The hypothesis is the domain on which Definition 1.1 defines .
import Definitions.Def_CandesTao_Decoding_RestrictedIsometry
namespace CandesTao.Decoding
theorem dual_reconstruction_linf {p m : ℕ} (F : Matrix (Fin p) (Fin m) ℝ) (S : ℕ)
(hS : 1 ≤ S) (hSm : 3 * S ≤ m)
(h : restrictedIsometryConst F S + restrictedOrthogonalityConst F S (2 * S) < 1)
(T : Finset (Fin m)) (c : Fin m → ℝ) (hT : T.card ≤ S) (hc : SupportedOn c T) :
∃ w : Fin p → ℝ, w ∈ columnSpan F ∧ (∀ j ∈ T, dotProduct w (column F j) = c j) ∧
∀ j, j ∉ T →
|dotProduct w (column F j)| ≤
restrictedOrthogonalityConst F S S /
((1 - restrictedIsometryConst F S - restrictedOrthogonalityConst F S (2 * S)) *
Real.sqrt S) * l2Norm c := by sorry
end CandesTao.DecodingRead-back
What the Lean code literally says, in plain math · claude-fable-5-1
Read-back of dual_reconstruction_linf
Objects and notation. Throughout, and are natural numbers (the binders allow either to be ), is a real matrix with rows indexed by and columns by , and is a natural number. For a vector write
where is Mathlib's real square root (it returns on negative inputs, which cannot occur here because the argument is a sum of squares); so is the ordinary Euclidean norm, and . For , denotes the -th column of , i.e. . For , is the standard dot product; in particular . is the usual matrix–vector product, . A vector is said to be supported on a finite set if
nothing is required of for (so is supported on every , and may vanish on part of ). The column span of is the -linear span of the set of columns , a linear subspace of (equivalently, ).
The restricted isometry constant. For a natural number ,
Here ranges over all subsets of size at most (including ), and over all vectors supported on (including ).
The restricted orthogonality constant. For natural numbers ,
Infimum convention. Both infima are Mathlib's on , which by convention returns for the empty set and for a set that has no lower bound. Each of the two sets above is bounded below by by construction, and each is nonempty for every finite matrix (every sufficiently large , resp. , belongs; e.g. works by Cauchy–Schwarz). So in both cases the infimum is the ordinary greatest lower bound, and , .
Hypotheses. The theorem assumes:
- , , as above;
- ;
- (hence ; no condition at all is placed on );
- (strict);
- a finite set with ;
- a vector supported on , i.e. for all .
Conclusion. There exists (at least one; uniqueness is not asserted) a vector such that all three of the following hold simultaneously:
(a) lies in the column span of ;
(b) for every :
(c) for every index with :
The right-hand side of (c) is parsed as , with meaning . The numerator is the orthogonality constant with parameters , whereas hypothesis 4 involves the one with parameters . is the real square root of the natural number regarded as a real number; since this is the ordinary positive root and .
Edge cases and conventions.
- Division. In Lean, for real . Under hypothesis 4, , and , so the denominator in (c) is strictly positive and the division-by-zero convention is not triggered; the fraction is the ordinary real quotient.
- is allowed by hypothesis 5; then , condition (b) is vacuous, and (c) demands for every . More generally is allowed with any , and need not be nonzero on all of .
- is allowed by the binders. Then is the zero space and for every ; since and , there is a nonzero supported on a singleton, so every admissible in the definition of must satisfy , i.e. . Hence , and as , hypothesis 4 cannot hold: for the statement is vacuously true.
- All cardinality conditions ( in hypothesis 5 and inside , ) are non-strict, and the final bound (c) is a non-strict inequality .
- No hypothesis constrains the rank of , the size of beyond , or the relation between and or beyond .
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.