Schur stability conjecture for image reconstruction operators
Schur stability conjecture for image reconstruction operators
Let be a primitive stochastic matrix, let be a positive semidefinite matrix satisfying
and
where is the left Perron eigenvector of . Define
Schur stability conjecture. The operators and are stable, meaning that
for all .
Progress summary
An unverified posted calculation claims that explicit two-dimensional examples disprove both stability assertions, while the published paper only proves stability for sufficiently small steps and selected cases.
Banerjee and Chaudhury posed the conjecture in September 2025: under the stated hypotheses, both reconstruction operators should remain stable throughout the prescribed step-size interval.
Known results
- Banerjee–Chaudhury (2025): both operators are stable for each sufficiently small , although the proof gives no uniform estimate for the threshold.
- Banerjee–Chaudhury (2025): explicit stability ranges are obtained for particular classes of and .
- Banerjee–Chaudhury (2025): the bound is shown necessary for the conjectured formulation; numerical examples indicate that the bound for need not be sharp.
Posted attempt
An unverified calculation claims that a positive-definite matrix and primitive stochastic matrices satisfy all hypotheses while both operators have spectral radius greater than at . It further claims a parameterized family with instability for arbitrarily small positive , which would refute both assertions rather than merely prove partial progress. The calculation has not been independently verified.
Current status (as of August 2026): the local and selected special-case results are established, while the general conjecture is now accompanied by an unverified claimed counterexample and is not mathematically settled.
Sources
Sources & referencesView supporting material
Primary source
Debraj Banerjee and Kunal Narayan Chaudhury, “On the Schur Stability of Some Image Reconstruction Operators”, arXiv:2509.06715 (2025).
Solutions 1
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Counterexample to both stability assertions, with arbitrarily small instability thresholds.
Conjecture 1.1 of Banerjee and Chaudhury, arXiv:2509.06715, considers
where is primitive and row-stochastic, is positive semidefinite, , and the normalized left Perron vector satisfies
It asserts that both spectral radii are strictly less than one throughout . The conjecture does not require to be entrywise nonnegative: the preceding discussion explicitly says that nonnegativity is not essential, and Example 1.2 includes negative off-diagonal entries.
Take
Indeed,
so is primitive. Furthermore,
Thus is even positive definite. The stationary vector is
and .
Exact rational calculation gives
Nevertheless,
For each real two-by-two matrix , its monic characteristic polynomial therefore has and as . Each matrix consequently has a real eigenvalue strictly greater than one:
Hence both claimed stability assertions fail simultaneously.
In fact the obstruction occurs arbitrarily close to zero. Keep the same positive definite , choose any , and put
v=\frac1{5-s},\qquad W_s=\begin{pmatrix}0&1\v&1-v\end{pmatrix}, \qquad \pi_s=\frac1{1+v}\binom v1.Then is primitive and stochastic, , and
Direct symbolic calculation yields
At , both operators already have eigenvalue one, and for every , both have a real eigenvalue greater than one. Since can be arbitrarily small while remains fixed, no positive stability threshold depending only on and the dimension is valid under the conjectured assumptions. The paper's separate local per-instance stability theorem remains unaffected.