How statement and proof provenance work
The first chip identifies the source of the statement or construction; the second identifies the source of its local proof or verification.
- Literature-sourced: the exact statement appears in a cited source; only wording and notation differ.
- AI-adapted: a semantically identical restatement of literature-sourced material, modulo indexing, notation, and boundary cases adopted by the library.
- AI-generated: a genuinely novel statement formulated by AI, with no source for the claim itself.
These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Real phase retrieval is equivalent to the complement property
Statement
Let , , with , and let be a finite family in . Use the standard dot product , identify when or , and write for the resulting class. Define , a formula independent of the representative because replacing by leaves every square unchanged. For , let be the set of finite real linear combinations of its vectors, with the empty span equal to . Then is injective on the global-sign classes if and only if for every at least one of and equals .
Statement references
Facts & Assumptions
Given: A finite real family in , where and .
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 is .
The real sign quotient and its induced measurement maps: the induced intensity map is well defined on those classes, and exactly when or .
Complement property for a real finite measurement family: the complement property requires that for each , the span indexed by or the span indexed by its complement is all of .
The Euclidean inner product on , Real and complex inner-product spaces and their induced length, The standard bilinear form on , Orthogonality and the orthogonal complement: the real coordinate pairing is bilinear, symmetric, and positive definite, so it is a real inner product; means its pairing with every vector of is zero.
Linear combination of a finite list, and the span as the smallest linear subspace containing , Linear subspace of a vector space, is exactly the set of linear combinations of finite lists of elements of , and : 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.
If and is a linear subspace of , then is finite-dimensional, , and if and only if , Finite-dimensional vector space, and its dimension ; infinite-dimensional means having no finite basis, Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis, The standard list with and for is an ordered basis of ; hence , and is the zero space with basis and dimension : every subspace of is finite-dimensional with dimension at most , equality holds only for the whole space, and , including ; finite dimension supplies a finite basis.
Linear map between vector spaces over the same field, Kernel and image of a linear map, The kernel and image are linear subspaces, and a linear map is injective if and only if its kernel is trivial, Rank and nullity of a linear map with finite-dimensional domain, Rank-nullity: : for a linear map with finite-dimensional domain, its kernel and image are subspaces, its rank is the image dimension, and domain dimension is nullity plus rank.
The reals form a field, The reals form a totally ordered field: in , so and cannot annihilate a nonzero real vector.
No form of the axiom of Choice is assumed; the dimension results are finite-dimensional and the selections in the proof are finite.
Proof
To prove that the complement property implies injectivity, assume the complement property [F3] and suppose . At every label , equality of the intensity coordinates gives .
Factoring the difference of squares over yields for every . This factorization also holds when , , or ; no nonzero-signal or nonzero-measurement hypothesis is used.
Put . For , ; for , the first factor in step 2.1 is nonzero, so . 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.
By the complement property [F3], either or . In the first case step 3.1 makes orthogonal to every vector of , hence to itself; positive definiteness [F4] gives . In the second case it gives . Thus by [F2], proving injectivity.
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 has the proper subspaces and . We prove that every proper subspace has a nonzero orthogonal vector. Let by [F6], choose a basis of , and define by . Bilinearity makes linear. Its kernel is : vanishing against the basis implies vanishing against every vector of because the basis spans and [F5] expresses each span vector as a finite linear combination; the converse is immediate. By [F7], the image is a subspace of , so [F6] gives . Rank-nullity [F7] and then give . By [F6] a zero subspace has dimension zero, so contains a nonzero vector. Apply this argument to and and choose nonzero and .
Set and . Since , would imply , impossible by [F8]; since , would imply , also impossible. Hence by [F2].
If , then and , so and . If , then and , so both measurements equal . If , it lies in and both measurements are zero. Squaring shows equality of the intensity coordinates at every label, including when either component of the partition is empty.
When , the only partition has both sides empty, and both spans equal by [F5] and . 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.
Steps 6.1 and 6.2 give distinct quotient classes with the same intensity data, so is not injective. Together with step 4.1 and the definition of phase retrieval [F1], this proves the stated equivalence.
Depends on
- Real phase retrieval via finite real measurements
- The real sign quotient and its induced measurement maps
- Complement property for a real finite measurement family
- The Euclidean inner product $\langle x,y\rangle = \sum_{k<n} x_k y_k$ on $\mathbb{R}^n$
- Real and complex inner-product spaces and their induced length
- Orthogonality and the orthogonal complement
- The standard bilinear form $\langle x,y\rangle=\sum_{i<n}x_iy_i$ on $F^{n}$
- Linear subspace of a vector space
- Linear combination of a finite list, and the span $\operatorname{span}(S)$ as the smallest linear subspace containing $S$
- $\operatorname{span}(S)$ is exactly the set of linear combinations of finite lists of elements of $S$, and $\operatorname{span}(\varnothing) = \{0_V\}$
- Basis of a vector space: a linearly independent spanning subset; and ordered basis: an injective finite list whose image is a basis
- If $\dim_F V = n$ and $U$ is a linear subspace of $V$, then $U$ is finite-dimensional, $\dim_F U \le n$, and $\dim_F U = n$ if and only if $U = V$
- Finite-dimensional vector space, and its dimension $\dim_F V$; infinite-dimensional means having no finite basis
- The standard list $e : n \to F^{n}$ with $e_i(i) = 1_F$ and $e_i(j) = 0_F$ for $j \ne i$ is an ordered basis of $F^{n}$; hence $\dim_F F^{n} = n$, and $F^{0}$ is the zero space with basis $\varnothing$ and dimension $0$
- Linear map between vector spaces over the same field
- Kernel and image of a linear map
- The kernel and image are linear subspaces, and a linear map is injective if and only if its kernel is trivial
- Rank and nullity of a linear map with finite-dimensional domain
- Rank-nullity: $\dim_F V=\operatorname{nullity}T+\operatorname{rank}T$
- The reals form a field
- The reals form a totally ordered field
Used by
Dependency tree · two levels
85 results within two dependency steps of this one, each drawn at its shortest distance from it. An arrow runs from a result to what uses it, so the chart reads left to right and ends at this result, which carries a heavier outline. Every node is a link to that result. Click elsewhere on the chart to enlarge it.
Sources
- Radu Balan, Pete Casazza, and Dan Edidin, On signal reconstruction without phase, Applied and Computational Harmonic Analysis 20(3) (2006), Theorem 2.8 (standard reference, not scraped)
- Philipp Grohs, Sarah Koppensteiner, and Martin Rathmair, Phase Retrieval: Uniqueness and Stability, arXiv:1901.07911v2, Theorem 2.2 and proof (standard reference, not scraped)
- Radu Balan, Frames and Phaseless Reconstruction, full lecture notes (2015), Theorem 3.2 (standard reference, not scraped)