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Real phase retrieval and phaseless ambiguity — Examples and Counterexamples

1 · Prerequisites

2 · Logical flowchart

3 · Definitions, theorems and proofs

None yet.

4 · Examples, counterexamples and false statements

ExampleConstruction: Literature-sourcedVerification: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

Translation and reflection ambiguities for a finite Fourier magnitude map

Example

Let C3=Z/3Z, put ω=(−1−i3)/2, and for a real signal f:C3→R define its unnormalised discrete Fourier transform by f^(k)=∑n=02f(n)ωkn for the representatives k=0,1,2. Its Fourier magnitude data are (∣f^(k)∣)k=02. Define translation by (τ1f)(n)=f(n−1) and reflection by (Rf)(n)=f(−n), with indices in C3. For f(0)=1, f(1)=2, and f(2)=0, both τ1f and Rf have the same Fourier magnitude data as f, while neither equals f or −f.

Facts & Assumptions

Given: The cyclic group C3, ω=(−1−i3)/2, the stated DFT convention, and the real signal f in the example.

Verification

1.1givenF1F2algebra

Write s=3. By [F1] and [F2], ω2=(−1+is)/2, ω3=1, and ∣ω∣2=1, hence ∣ω∣=1. Reindexing the finite sum with j=n−1 gives τ1f^(k)=∑jf(j)ωk(j+1)=ωkf^(k). Multiplicativity of the modulus [F1] gives ∣ωk∣=1, so every Fourier magnitude is unchanged.

2.1step 1.1F1algebra

Since ∣ω∣=1, [F1] gives ω‾=ω−1. Reindexing with j=−n gives Rf^(k)=∑jf(j)ω−kj. Because every f(j) is real, this equals ∑jf(j)ωkj‾=f^(k)‾ by the conjugation laws in [F1], so reflection preserves every Fourier magnitude.

3.1step 1.1step 2.1givenconstructalgebra∎

The support of f and −f is {0,1}, while the supports of τ1f and Rf are {1,2} and {0,2} respectively. Thus neither transformed signal equals f or −f. Combined with the invariances in steps 1.1 and 2.1, both transformed signals are distinct from the original up to global sign but have identical Fourier magnitude data.

CounterexampleConstruction: AI-adaptedVerification: AI-adaptedverified 2026-10-22 (gpt-6.1-sol)Open item page →

A two-vector real family with a failed partition and an ambiguous pair

Statement refuted

In R2 with u⋅v=u0v0+u1v1, take the real measurement family Φ=(a1,a2) with a1=(1,0) and a2=(0,1), and define its intensity data by IΦ(x)=((a1⋅x)2,(a2⋅x)2). The partition S={1} has neither side spanning R2. The signals x=(1,1) and z=(−1,1) satisfy neither z=x nor z=−x, but IΦ(x)=IΦ(z).

Facts & Assumptions

Given: R2 with the standard dot product, the family a1=(1,0) and a2=(0,1), and the global-sign quotient used for real phase retrieval.

[F1]

Real phase retrieval via finite real measurements: intensity coordinates are ⟨ai,x⟩2, and the target identifies x with −x.

[F2]

Complement property for a real finite measurement family: the complement property requires one side of every label partition to span the full ambient space.

[F3]

The real sign quotient and its induced measurement maps: two real signal classes agree exactly when their representatives are equal or negatives.

[F4]

Linear combination of a finite list, and the span span⁡(S) as the smallest linear subspace containing S, Linear subspace of a vector space: each span is the smallest linear subspace containing its measurement vectors; in particular, coordinate axes are subspaces and spans are closed under scalar multiplication.

Counterexample

1.1givenF2F4algebra

Let E1={(t,0):t∈R} and E2={(0,t):t∈R}; coordinatewise addition and scalar multiplication show these are subspaces. Since a1∈E1, minimality of the span gives span⁡{a1}⊆E1, while every (t,0)=ta1 belongs to the span because it is a subspace containing a1. Thus span⁡(Φ{1})=E1. The same two inclusions for a2 give span⁡(Φ{2})=E2. Both are proper subspaces of R2, so [F2] says the complement property fails.

1.2givenF3algebra

The vectors (1,1) and (−1,1) are neither equal nor negatives: equality fails in the first coordinate, while equality with the negative fails in the second coordinate. Thus they define distinct signal classes by [F3].

2.1step 1.1step 1.2F1algebra∎

Their measurements are ((1,1)⋅a1,(1,1)⋅a2)=(1,1) and ((−1,1)⋅a1,(−1,1)⋅a2)=(−1,1). Squaring coordinatewise gives (1,1) in both cases, so [F1] gives equal intensity data on the distinct classes from step 1.2. Together with the failed partition in step 1.1, this exhibits the required ambiguity when the complement property fails.

ExampleConstruction: AI-generatedVerification: AI-generatedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

A one-pattern sign budget misses the best amplitude fit

Example

Let d=1, m=2, A=(1,−1)T, and b=(1,4), so r=(1,2). In noisy sign enumeration, fix the first sign to +1, order the remaining signs with +1 before −1, and use P=L=1. The algorithm processes only (+1,+1), returns x=−1/2 with J(x)=∑i=12(∣(Ax)i∣−ri)2=5/2, and reports budget-exhausted. The unprocessed pattern (+1,−1) has least-squares candidate x=3/2 with J(x)=1/2.

Facts & Assumptions

Given: The matrix A=(1,−1)T, data b=(1,4), noisy mode, and budgets P=L=1.

[F1]

Finite sign enumeration for real phase retrieval: for m=2, the canonical signs are ordered (+,+) then (+,−); a pattern cap or solve budget that stops before both are processed returns budget-exhausted and the noisy objective is J(x)=∑i(∣(Ax)i∣−ri)2.

[F2]

Correctness and amplitude-residual objective of finite sign enumeration: each processed signed candidate minimizes its signed least-squares residual, while a global-minimum guarantee is made only after every canonical pattern is processed.

Verification

1.1givenF1

Since m=2, the canonical sign set is exactly (+,+),(+,−) in the stated order. Both budgets permit one solve, so only (+,+) is processed and the algorithm reports budget-exhausted.

1.2givenF1F2algebra

For the processed target y++=(1,2), the squared signed residual is (x−1)2+(−x−2)2=2(x+1/2)2+9/2, minimized uniquely at x=−1/2 by [F2]. Its amplitudes are (1/2,1/2), so J(−1/2)=(1/2−1)2+(1/2−2)2=5/2.

2.1F2step 1.2algebra∎

For the unprocessed target y+−=(1,−2), the squared signed residual is (x−1)2+(−x+2)2=2(x−3/2)2+1/2, minimized uniquely at x=3/2 by [F2]. Its amplitudes are (3/2,3/2), so J(3/2)=(3/2−1)2+(3/2−2)2=1/2<5/2. Thus the budget-limited incumbent is not the best candidate over all patterns.

ExampleConstruction: AI-adaptedVerification: AI-adaptedverified 2026-10-22 (gpt-6.1-sol)Open item page →

A two-dimensional three-vector full-spark real frame

Example

In R2 take Φ=(a1,a2,a3) with a1=(1,0), a2=(0,1), and a3=(1,1). Its intensity map on classes modulo x∼−x is [x]↦(x02,x12,(x0+x1)2). Every pair is linearly independent, so every pair spans R2; consequently, for every partition one side spans R2. Equality of the displayed triples therefore forces two signals to be equal up to a global sign.

Facts & Assumptions

Given: The family a1=(1,0), a2=(0,1), a3=(1,1) in R2 and the real sign-quotient intensity map.

[F2]

Linear combination of a finite list, and the span span⁡(S) as the smallest linear subspace containing S, Linear subspace of a vector space: every span is a subspace containing the listed vectors and is closed under addition and scalar multiplication.

[F3]

The sharp real phase-retrieval count for full-spark measurements: a full-spark real family with at least 2d−1 measurements performs phase retrieval.

Verification

1.1givenF1F2algebra

For every (u,v)∈R2, (u,v)=ua1+va2=(u−v)a1+va3=ua3+(v−u)a2, so each pair spans R2 by [F2]. For independence, a vanishing combination of a1,a2 has coordinates (α,β)=(0,0); a vanishing combination αa1+βa3 has coordinates (α+β,β)=(0,0), forcing β=α=0; and a vanishing combination αa2+βa3 has coordinates (β,α+β)=(0,0), again forcing α=β=0. Thus every pair is linearly independent, so [F1] gives full spark.

2.1step 1.1F3algebra∎

Here d=2 and m=3=2d−1. Applying [F3] to step 1.1 proves phase retrieval on the stated global-sign quotient; the intensity coordinates are exactly the three squared inner products displayed in the example.

CounterexampleConstruction: Literature-sourcedVerification: AI-adaptedjudge pass (gpt-6-luna)verified 2026-10-22 (gpt-6.1-sol)Open item page →

The complement property is not sufficient for complex phase retrieval

Statement refuted

In C2 use a1=(1,0), a2=(0,1), and a3=(1,1), and define ⟨a,x⟩=a(1)‾x1+a(2)‾x2 for a=(a(1),a(2)). Identify signals by global phase, x≈z when z=λx for some λ∈C with ∣λ∣=1, and use intensity data (∣⟨aj,x⟩∣2)j=13. Here the complex complement property means that every subset of the three labels or its complement spans C2. Each pair spans C2, so the family has this property; nevertheless, x=(1,i) and z=(1,−i) have equal intensity data and are not related by a global phase. Thus the real complement-property criterion and its real full-spark count cannot be transferred to complex phase retrieval.

Facts & Assumptions

Given: The three vectors in C2, the Hermitian pairing and global-phase relation defined in the counterexample, and the real-field scope of the cited theorem.

[F1]

Real phase retrieval is equivalent to the complement property: the complement-property equivalence proved here has hypotheses over Rd and identifies only the pair x,−x.

[F2]

The sharp real phase-retrieval count for full-spark measurements: the 2d−1 full-spark sufficiency statement is for finite real measurement families.

[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: each span is a complex linear subspace containing its generators, so every displayed linear combination belongs to that span.

Counterexample

1.1givenF3F5algebra

For arbitrary (u,v)∈C2, the three identities (u,v)=ua1+va2=(u−v)a1+va3=ua3+(v−u)a2 show that each of the pairs {a1,a2}, {a1,a3}, and {a2,a3} spans C2: the span is a subspace containing the pair, so it contains the corresponding displayed combination for every (u,v). Any partition of three labels has at least two labels on one side, and a set containing a spanning pair also spans; hence the complex complement property holds.

1.2givenF3F4algebra

The measurements of x=(1,i) are (1,i,1+i) and those of z=(1,−i) are (1,−i,1−i). By [F3] and [F4], their squared moduli are (1,1,2) in both cases.

2.1step 1.1step 1.2F1F2F3algebra∎

If z=λx with ∣λ∣=1, the first coordinate forces λ=1, while the second would then force −i=i, which is false in the field [F3]. Thus x≉z, and step 1.2 proves noninjectivity despite step 1.1. By [F1] and [F2], this does not contradict either real-field result: the ambient field, phase relation, and measurements differ.

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