arXiv:2111.15410v2

Dimension estimates for badly approximable affine forms

Taehyeong Kim, Wooyeon Kim, Seonhee Lim

math.DSmath.NT11J2028D2037A17

Abstract

For given ε>0ε>0 and bRmb\in\mathbb{R}^m, we say that a real m×nm\times n matrix AA is εε-badly approximable for the target bb if lim infqZn,qqnAqbmε,\liminf_{q\in\mathbb{Z}^n, \|q\|\to\infty} \|q\|^n \langle Aq-b \rangle^m \geq ε, where \langle \cdot \rangle denotes the distance from the nearest integral point. In this article, we obtain upper bounds for the Hausdorff dimensions of the set of εε-badly approximable matrices for fixed target bb and the set of εε-badly approximable targets for fixed matrix AA. Moreover, we give an equivalent Diophantine condition of AA for which the set of εε-badly approximable targets for fixed AA has full Hausdorff dimension for some ε>0ε>0. The upper bounds are established by effectivizing entropy rigidity in homogeneous dynamics, which is of independent interest. For the AA-fixed case, our method also works for the weighted setting where the supremum norms are replaced by certain weighted quasinorms.

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Audited against arXiv v2

Not a correctness certificate. A “Correct” result may include yellow typos or minor formal corrections that do not affect substantive soundness. It means this audit found no unresolved substantive error under the stated criteria; it does not replace expert scrutiny or formal verification.

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Generated August 19, 2026
01Statements4 reported findingsCorrect

The dimension deficits for fixed targets and fixed matrices and the singular-on-average characterization are correct after two local parameter-range corrections. One quasinorm subscript is a harmless typo.

Definition (1.2)Typo

The denominator quasinorm has the wrong subscript

Page 2 · equation (1.2) · arXiv:2111.15410v2

The limit variable is qZn\mathbf q\in\mathbb Z^n, but the approach to infinity is printed as qr\|\mathbf q\|_{\mathbf r}\to\infty. The r\mathbf r-quasinorm is defined on Rm\mathbb R^m, whereas all other factors and every later use employ the s\mathbf s-quasinorm on Rn\mathbb R^n. Replace the subscript r\mathbf r by s\mathbf s.

Theorem 1.1Minor formal correction

The effective fixed-target estimate is a small-epsilon statement

Page 3 · Theorem 1.1; pages 39–43 · its proof · arXiv:2111.15410v2

The theorem prints the bound dimHBadb(ε)mnc0εM0\dim_H\operatorname{Bad}^{\mathbf b}(\varepsilon)\leq mn-c_0\varepsilon^{M_0} for every ε>0\varepsilon>0. For sufficiently large ε\varepsilon the right side is negative, while for a nonintegral target b\mathbf b the matrix A=0A=0 belongs to Badb(ε)\operatorname{Bad}^{\mathbf b}(\varepsilon) for every ε\varepsilon, so the unrestricted display cannot hold. The proof uses its effective mixing estimates only for sufficiently small ε\varepsilon. Replace ‘for any ε>0\varepsilon>0’ by ‘for all sufficiently small ε>0\varepsilon>0’; no later result uses the excluded range.

Theorem 1.2Minor formal correction

The logarithmic estimate needs 0<ε<10<\varepsilon<1

Page 4 · Theorem 1.2; pages 31–34 · its proof · arXiv:2111.15410v2

The displayed term ε/log(1/ε)\varepsilon/\log(1/\varepsilon) is undefined at ε=1\varepsilon=1, although the theorem is printed for every ε>0\varepsilon>0. The entropy proof uses logε-\log\varepsilon as a positive scale and establishes the nontrivial estimate for 0<ε<10<\varepsilon<1. State that range explicitly.

Theorem 1.3Correct

The singular-on-average equivalence is correct

Pages 4 and 43–49 · Theorem 1.3 · arXiv:2111.15410v2

The effective deficit proves that a matrix not singular on average cannot have a full-dimensional fixed-target bad set. In the reverse direction, the best-approximation construction uses the density-one singular scales to build a Cantor subset of full dimension for one positive badness constant.

02Proofs2 reported findingsCorrect

The measure-construction, entropy-rigidity, effective mixing, and best-approximation proofs are correct and complete in the corrected parameter ranges.

Sections 3–5Correct and complete

The two effective entropy estimates close

Pages 13–43 · Sections 3–5 · arXiv:2111.15410v2

The limiting affine-lattice measures retain the quantified mass needed for conditional entropy, the subordinate-partition estimates produce the asserted entropy deficit, and effective equidistribution supplies constants independent of the target in Theorem 1.1.

Section 6Correct and complete

The constructive direction of Theorem 1.3 is complete

Pages 43–49 · Section 6 · arXiv:2111.15410v2

The chosen best-approximation subsequence has the required growth and orthogonality, forbidden neighborhoods are removed with controlled proportion, and the remaining nested sets support a full-dimensional measure of targets satisfying the uniform lower approximation bound.

03Novelty0 reported findingsNo non-novelty findings

No non-novelty findings.

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Paper
arXiv:2111.15410v2
Authors listed
Taehyeong Kim, Wooyeon Kim, Seonhee Lim
Audit date
August 19, 2026
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