Alphabeta Math
TheoremStatement: Literature-sourcedProof: AI-adaptedPipeline-generatedverified 2026-10-22 (gpt-6.1-sol)
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Real phase retrieval is equivalent to the complement property

Statement

Let d≥1, m≥0, [m]={1,…,m} with [0]=∅, and let Φ=(ai)i∈[m] be a finite family in Rd. Use the standard dot product ⟨u,v⟩=∑k<dukvk, identify x,z∈Rd when z=x or z=−x, and write [x] for the resulting class. Define IΦ([x])=(⟨ai,x⟩2)i∈[m], a formula independent of the representative because replacing x by −x leaves every square unchanged. For S⊆[m], let span⁡R(ΦS) be the set of finite real linear combinations of its vectors, with the empty span equal to {0}. Then IΦ is injective on the global-sign classes if and only if for every S⊆[m] at least one of span⁡R(ΦS) and span⁡R(Φ[m]∖S) equals Rd.

Facts & Assumptions

Given: A finite real family Φ=(ai)i∈[m] in Rd, where d≥1 and m≥0.

[F1]

Real phase retrieval via finite real measurements: phase retrieval means injectivity of the intensity map on classes modulo one global sign; the real measurement at label i is ⟨ai,x⟩.

[F2]

The real sign quotient and its induced measurement maps: the induced intensity map is well defined on those classes, and [x]=[z] exactly when x=z or x=−z.

[F3]

Complement property for a real finite measurement family: the complement property requires that for each S⊆[m], the span indexed by S or the span indexed by its complement is all of Rd.

[F4]
[F5]

Linear combination of a finite list, and the span span⁡(S) as the smallest linear subspace containing S, Linear subspace of a vector space, span⁡(S) is exactly the set of linear combinations of finite lists of elements of S, and span⁡(∅)={0V}: the library span is the smallest real linear subspace containing its generators and equals the set of their finite real linear combinations. Bilinearity therefore extends orthogonality from the generators to their span.

[F8]

The reals form a field, The reals form a totally ordered field: 2=1+1>0 in R, so 2≠0 and cannot annihilate a nonzero real vector.

[A1]

No form of the axiom of Choice is assumed; the dimension results are finite-dimensional and the selections in the proof are finite.

Proof

1.1givenF1F3

To prove that the complement property implies injectivity, assume the complement property [F3] and suppose IΦ([x])=IΦ([z]). At every label i, equality of the intensity coordinates gives ⟨ai,x⟩2=⟨ai,z⟩2.

2.1step 1.1F4F8algebra

Factoring the difference of squares over R yields (⟨ai,x−z⟩)(⟨ai,x+z⟩)=0 for every i∈[m]. This factorization also holds when x=0, z=0, or ai=0; no nonzero-signal or nonzero-measurement hypothesis is used.

3.1step 2.1F4F5construct

Put S={i∈[m]:⟨ai,x−z⟩=0}. For i∈S, ai⊥(x−z); for i∉S, the first factor in step 2.1 is nonzero, so ai⊥(x+z). By [F5], each vector in either span is a finite linear combination of its generators, and bilinearity extends each orthogonality statement to the whole span.

4.1step 3.1F2F3F4F5algebra

By the complement property [F3], either span⁡(ΦS)=Rd or span⁡(Φ[m]∖S)=Rd. In the first case step 3.1 makes x−z orthogonal to every vector of Rd, hence to itself; positive definiteness [F4] gives x−z=0. In the second case it gives x+z=0. Thus [x]=[z] by [F2], proving injectivity.

5.1givenF5F6F7A1constructchoose

Step 4.1 proves that the complement property implies injectivity. For injectivity implying the complement property, argue contrapositively and suppose the complement property fails; then some S⊆[m] has the proper subspaces U=span⁡(ΦS) and V=span⁡(Φ[m]∖S). We prove that every proper subspace W≤Rd has a nonzero orthogonal vector. Let r=dim⁡W<d by [F6], choose a basis w0,…,wr−1 of W, and define LW:Rd→Rr by LW(x)=(⟨x,wj⟩)j<r. Bilinearity makes LW linear. Its kernel is W⊥: vanishing against the basis implies vanishing against every vector of W because the basis spans W and [F5] expresses each span vector as a finite linear combination; the converse is immediate. By [F7], the image is a subspace of Rr, so [F6] gives rank⁡LW=dim⁡(im⁡LW)≤r. Rank-nullity [F7] and dim⁡Rd=d then give dim⁡W⊥=dim⁡ker⁡LW=d−rank⁡LW≥d−r>0. By [F6] a zero subspace has dimension zero, so W⊥ contains a nonzero vector. Apply this argument to U and V and choose nonzero u∈U⊥ and v∈V⊥.

6.1step 5.1F2F8algebra

Set x=u+v and z=u−v. Since v≠0, x=z would imply 2v=0, impossible by [F8]; since u≠0, x=−z would imply 2u=0, also impossible. Hence [x]≠[z] by [F2].

6.2step 5.1F4F5algebra

If i∈S, then ai∈U and u⊥ai, so ⟨ai,x⟩=⟨ai,v⟩ and ⟨ai,z⟩=−⟨ai,v⟩. If i∉S, then ai∈V and v⊥ai, so both measurements equal ⟨ai,u⟩. If ai=0, it lies in U and both measurements are zero. Squaring shows equality of the intensity coordinates at every label, including when either component of the partition is empty.

7.1F2F5F6F7step 5.1step 6.1step 6.2

When m=0, the only partition has both sides empty, and both spans equal {0}≠Rd by [F5] and d≥1. The complement property fails, while steps 5.1–6.2 give distinct global-sign classes with the same unique empty data. Thus the zero-measurement case is included.

8.1step 4.1step 6.1step 6.2F1algebra∎

Steps 6.1 and 6.2 give distinct quotient classes with the same intensity data, so IΦ is not injective. Together with step 4.1 and the definition of phase retrieval [F1], this proves the stated equivalence.

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