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These labels describe origin, not correctness: citations and verification chips remain separate evidence.
Real phase retrieval and phaseless ambiguity — Examples and Counterexamples
1 · Prerequisites
- Algebraic Closure, Embeddings, and Separability
- Algebraic Extensions, Extension Degree, and Finite Fields
- Binary Operations, Monoids, Groups and Subgroups
- Complex Norms and Realification
- Composition Series, the Jordan–Hölder Theorem and Solvable Groups
- Congruences, the Integers Modulo n and the Chinese Remainder Theorem
- Construction of the Natural Numbers
- Construction of the Real Numbers via Cauchy Sequences
- Construction of the Real Numbers via Dedekind Cuts
- Continuity, IVT, EVT, and Uniform Continuity
- Cosets, Index and Lagrange's Theorem
- Countability and Uncountability
- Cyclic Groups and Direct Products
- Determinants of Matrices over a Commutative Ring
- Diagonalisation and the Minimal Polynomial
- Divisibility, Euclidean Domains, Principal Ideal Domains and Unique Factorisation
- Divisibility, Greatest Common Divisors and Bézout's Identity
- Dual Spaces, Bilinear and Quadratic Forms, and Sylvester's Law of Inertia
- Eigenvalues, Eigenvectors and the Characteristic Polynomial
- Finite Counting, Factorials and Binomial Coefficients
- Foundations of the Real Numbers for Analysis
- Gaussian Elimination, Elementary Matrices and Reduced Row Echelon Form
- Group Actions, Orbits, Stabilisers and Cayley's Theorem
- Group Homomorphisms and the Isomorphism Theorems
- Hilbert Space Geometry and Riesz Representation
- Ideals, Quotient Rings and the Isomorphism Theorems for Rings
- Inner Product Spaces, Gram-Schmidt, Projections and Adjoints
- Inverse maps, identifiability, and stability
- Limits of Real Functions
- Linear Algebra Methods in Combinatorics
- Linear Independence, Bases and Dimension
- Linear Transformations, Rank-Nullity and Quotient Spaces
- Matrices, the Matrix of a Linear Map, and Change of Basis
- Metric Spaces
- Monotone Sequences, Bolzano-Weierstrass, and Cauchy Completeness
- Normal Subgroups and Quotient Groups
- Order, Zorn's Lemma, and the Axiom of Choice
- Polynomial Rings, the Division Algorithm and Roots
- Primes, Euclid's Lemma and the Fundamental Theorem of Arithmetic
- Real phase retrieval and phaseless ambiguity
- Relations, Functions, and Quotients
- Rings, Subrings, Integral Domains and Fields
- Rⁿ as a Normed Space; Vector-Valued Functions
- Roots, Rational Powers, and Classical Inequalities
- Sequences and Limits
- Simple Field Extensions and the Construction of the Complex Numbers
- Splitting Fields
- Suprema and Infima
- Sylow's Theorems, p-Groups and Nilpotent Groups
- Symmetric Groups, Cycle Decomposition and the Sign Homomorphism
- The Determinant of a Linear Operator, Cofactors and Cramer's Rule
- The Fundamental Theorem of Algebra
- The Fundamental Theorem of Finite Abelian Groups
- The Galois Correspondence
- The Moore--Penrose Pseudoinverse and Regularised Least Squares
- The Spectral Theorem, Positive Operators and Singular Value Decomposition
- The ZFC Axioms and the Basic Set Constructions
- Topology of ℝ
- Triangularisation, Generalised Eigenspaces and Jordan Canonical Form
- Vector Spaces, Linear Subspaces, Span and Direct Sums
2 · Logical flowchart
3 · Definitions, theorems and proofs
None yet.
4 · Examples, counterexamples and false statements
Translation and reflection ambiguities for a finite Fourier magnitude map
Example
Let , put , and for a real signal define its unnormalised discrete Fourier transform by for the representatives . Its Fourier magnitude data are . Define translation by and reflection by , with indices in . For , , and , both and have the same Fourier magnitude data as , while neither equals or .
Example references
Facts & Assumptions
Given: The cyclic group , , the stated DFT convention, and the real signal in the example.
Complex arithmetic is a field operation system; conjugation and modulus are given by and , and conjugation respects sums, products and modulus ( is a field, every element is uniquely , and every nonzero element has inverse , Real and imaginary parts, complex conjugation, and modulus, Conjugation is an involutive real-field automorphism, , and modulus is definite, multiplicative, and subadditive).
The nonnegative square root of squares to (Square roots exist: a unique with ; the positives are ).
Verification
Write . By [F1] and [F2], , , and , hence . Reindexing the finite sum with gives . Multiplicativity of the modulus [F1] gives , so every Fourier magnitude is unchanged.
Since , [F1] gives . Reindexing with gives . Because every is real, this equals by the conjugation laws in [F1], so reflection preserves every Fourier magnitude.
The support of and is , while the supports of and are and respectively. Thus neither transformed signal equals or . 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.
A two-vector real family with a failed partition and an ambiguous pair
Statement refuted
In with , take the real measurement family with and , and define its intensity data by . The partition has neither side spanning . The signals and satisfy neither nor , but .
Statement references
Facts & Assumptions
Given: with the standard dot product, the family and , and the global-sign quotient used for real phase retrieval.
Real phase retrieval via finite real measurements: intensity coordinates are , and the target identifies with .
Complement property for a real finite measurement family: the complement property requires one side of every label partition to span the full ambient space.
The real sign quotient and its induced measurement maps: two real signal classes agree exactly when their representatives are equal or negatives.
Linear combination of a finite list, and the span as the smallest linear subspace containing , 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
Let and ; coordinatewise addition and scalar multiplication show these are subspaces. Since , minimality of the span gives , while every belongs to the span because it is a subspace containing . Thus . The same two inclusions for give . Both are proper subspaces of , so [F2] says the complement property fails.
The vectors and 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].
Their measurements are and . Squaring coordinatewise gives 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.
A one-pattern sign budget misses the best amplitude fit
Example
Let , , , and , so . In noisy sign enumeration, fix the first sign to , order the remaining signs with before , and use . The algorithm processes only , returns with , and reports budget-exhausted. The unprocessed pattern has least-squares candidate with .
Example references
Facts & Assumptions
Given: The matrix , data , noisy mode, and budgets .
Finite sign enumeration for real phase retrieval: for , 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 .
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
Since , 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.
For the processed target , the squared signed residual is , minimized uniquely at by [F2]. Its amplitudes are , so .
For the unprocessed target , the squared signed residual is , minimized uniquely at by [F2]. Its amplitudes are , so . Thus the budget-limited incumbent is not the best candidate over all patterns.
A two-dimensional three-vector full-spark real frame
Example
In take with , , and . Its intensity map on classes modulo is . Every pair is linearly independent, so every pair spans ; consequently, for every partition one side spans . Equality of the displayed triples therefore forces two signals to be equal up to a global sign.
Example references
Facts & Assumptions
Given: The family , , in and the real sign-quotient intensity map.
Full-spark real measurement families, Linear independence: a finite list is independent when forces every , and a subset is independent when every injective finite list into is independent: a three-vector family in is full spark when every two vectors are linearly independent.
Linear combination of a finite list, and the span as the smallest linear subspace containing , Linear subspace of a vector space: every span is a subspace containing the listed vectors and is closed under addition and scalar multiplication.
The sharp real phase-retrieval count for full-spark measurements: a full-spark real family with at least measurements performs phase retrieval.
Verification
For every , so each pair spans by [F2]. For independence, a vanishing combination of has coordinates ; a vanishing combination has coordinates , forcing ; and a vanishing combination has coordinates , again forcing . Thus every pair is linearly independent, so [F1] gives full spark.
Here and . 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.
The complement property is not sufficient for complex phase retrieval
Statement refuted
In use , , and , and define for . Identify signals by global phase, when for some with , and use intensity data . Here the complex complement property means that every subset of the three labels or its complement spans . Each pair spans , so the family has this property; nevertheless, and 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.
Statement references
Facts & Assumptions
Given: The three vectors in , the Hermitian pairing and global-phase relation defined in the counterexample, and the real-field scope of the cited theorem.
Real phase retrieval is equivalent to the complement property: the complement-property equivalence proved here has hypotheses over and identifies only the pair .
The sharp real phase-retrieval count for full-spark measurements: the full-spark sufficiency statement is for finite real measurement families.
is a field, every element is uniquely , and every nonzero element has inverse : is a field with imaginary unit satisfying .
Linear combination of a finite list, and the span as the smallest linear subspace containing , 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
For arbitrary , the three identities show that each of the pairs , , and spans : the span is a subspace containing the pair, so it contains the corresponding displayed combination for every . 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.
The measurements of are and those of are . By [F3] and [F4], their squared moduli are in both cases.
If with , the first coordinate forces , while the second would then force , which is false in the field [F3]. Thus , 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.
Sources
- Philipp Grohs, Sarah Koppensteiner, and Martin Rathmair, Phase Retrieval: Uniqueness and Stability, arXiv:1901.07911v2, Proposition 3.3
- Radu Balan, Pete Casazza, and Dan Edidin, On signal reconstruction without phase, Applied and Computational Harmonic Analysis 20(3) (2006), the partition construction in the converse proof of Theorem 2.8
- Radu Balan, Frames and Phaseless Reconstruction, full lecture notes (2015), Theorem 3.2 proof
- Radu Balan, Pete Casazza, and Dan Edidin, full-spark phase retrieval threshold, Corollary 2.6
- Radu Balan, Frames and Phaseless Reconstruction, full lecture notes (2015), Corollary 3.3
- Philipp Grohs, Sarah Koppensteiner, and Martin Rathmair, Phase Retrieval: Uniqueness and Stability, arXiv:1901.07911v2, complex-field discussion in §2.1
- Radu Balan, Pete Casazza, and Dan Edidin, On signal reconstruction without phase, Applied and Computational Harmonic Analysis 20(3) (2006), discussion following Theorem 2.8