arXiv:2111.15410v2
Abstract
For given and , we say that a real matrix is -badly approximable for the target if where denotes the distance from the nearest integral point. In this article, we obtain upper bounds for the Hausdorff dimensions of the set of -badly approximable matrices for fixed target and the set of -badly approximable targets for fixed matrix . Moreover, we give an equivalent Diophantine condition of for which the set of -badly approximable targets for fixed has full Hausdorff dimension for some . The upper bounds are established by effectivizing entropy rigidity in homogeneous dynamics, which is of independent interest. For the -fixed case, our method also works for the weighted setting where the supremum norms are replaced by certain weighted quasinorms.
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Detailed mathematical audit
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.
The denominator quasinorm has the wrong subscript
Page 2 · equation (1.2) · arXiv:2111.15410v2
The limit variable is , but the approach to infinity is printed as . The -quasinorm is defined on , whereas all other factors and every later use employ the -quasinorm on . Replace the subscript by .
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 for every . For sufficiently large the right side is negative, while for a nonintegral target the matrix belongs to for every , so the unrestricted display cannot hold. The proof uses its effective mixing estimates only for sufficiently small . Replace ‘for any ’ by ‘for all sufficiently small ’; no later result uses the excluded range.
The logarithmic estimate needs
Page 4 · Theorem 1.2; pages 31–34 · its proof · arXiv:2111.15410v2
The displayed term is undefined at , although the theorem is printed for every . The entropy proof uses as a positive scale and establishes the nontrivial estimate for . State that range explicitly.
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.
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.
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.