arXiv:2607.24161v1
Abstract
Let satisfy and . For every function with , we prove that the set of -singular vectors whose weighted shortest-vector function satisfies for all sufficiently large has Hausdorff dimension , equal to the full Hausdorff dimension of . For every and , a separate power schedule gives Hausdorff dimension for the weighted uniform approximation set with rate , whereas the lower-envelope theorem gives the same dimension for its complement in . We give a direct proof of the lower-envelope theorem by adapting the self-affine construction of Liao--Shi--Solan--Tamam to a variable sequence of return times and using an empty-denominator-window argument to control intermediate cusp excursions without further pruning the tree.
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01Statements3 reported findingsCorrect
The arbitrary lower-envelope theorem for weighted singular vectors, both dimension assertions for the prescribed subpower uniform-approximation rate, and the full-lattice consequence have the stated scopes and follow from the proved construction and the cited ambient-space dimension bounds.
Arbitrarily slow weighted divergence
Page 2 · Theorem 1.1; proof on pages 23–24 · arXiv:2607.24161v1
For an arbitrary positive with , Lemma 2.2 replaces it by a divergent nondecreasing -Lipschitz target satisfying . The variable-step tree uses , , and . Proposition 4.2 verifies all counting, nesting, separation, shrinking, and singularity hypotheses and makes the denominator interval in Lemma 4.1 empty. Hence every nonexceptional branch limit satisfies eventually. Proposition 3.13 gives dimension , while the known dimension of gives the matching upper bound.
Liao–Shi–Solan–Tamam, weighted singular-vector dimension ↗Subpower uniform-approximation rate and its complement
Page 3 · Theorem 1.2; proof on pages 24–27 · arXiv:2607.24161v1
For and , the power schedule and , with , retains the full branching dimension. The vertical return at level supplies an approximant with and weighted error at most . Uniformly for , this is at most for all large . Conversely, Corollary 6.1 applies Theorem 1.1 to a target , where , and obtains a full-dimensional subset with eventually. Both sets lie in , so the upper dimensions are also .
Kleinbock–Moshchevitin–Warren–Weiss, weighted uniform-approximation framework ↗Full-lattice lower-envelope dimension
Page 3 · Corollary 1.3 and the paragraph following it · arXiv:2607.24161v1
The construction is uniform for the compact family and therefore gives dimensions on each two-dimensional -slice. Slicing in the remaining expanding root direction adds one dimension. The five central or contracting local-product directions remain uniformly bounded after conjugation by ; the elementary comparison of Euclidean and weighted minima explains the paper's use of the stronger seed envelope and transfers the desired envelope to those products. This yields the lower bound . Solan's general-flow estimate gives the same dimension as an upper bound for the ambient divergent set, which contains the set in the corollary.
Solan, general-flow parametric geometry of numbers ↗02Proofs4 reported findingsCorrect
The proof chains are correct and complete. The balance-time reduction has the required quantifiers, the variable-step modification preserves the cited LSST count and self-affine dimension estimate, the global schedule excludes every possible intermediate bad excursion, and the two uniform-rate arguments use the weighted error functional with the correct inequality directions.
Balance functions and target regularization
Pages 3–5 · Section 2 · arXiv:2607.24161v1
For a primitive vector with and , its contribution is exactly . Monotonicity and the -Lipschitz property give , while vectors with bounded balance time have bounded away from zero by lattice discreteness and therefore cannot affect the eventual lower envelope. The infimal -Lipschitz regularization is nonnegative, nondecreasing, divergent, and satisfies , with every endpoint and eventual quantifier preserved.
Full paper, version 1 ↗Variable-step counting and dimension
Pages 7–19 · Sections 3.2–3.5 · arXiv:2607.24161v1
Lemma 3.6 bijects candidate centers with primitive points in the required shell. Lemmas 3.7–3.8 put the three comparison boxes within the cited primitive lattice-point estimate. Lemma 3.5 injects each illegal class into the corresponding LSST exceptional set; after choosing , , and in that order, their total is a fixed fraction of the main term. The dual-systole condition supplies sibling separation. In the dimension calculation, , the long/short crossover obeys , and the separation loss is , giving every in the quoted self-affine criterion.
Liao–Shi–Solan–Tamam, source of the quoted counting and self-affine estimates ↗Denominator localization and empty windows
Pages 19–23 · Section 4 · arXiv:2607.24161v1
The parent dual-systole bound gives a uniform lower bound for the Euclidean first minimum at time . If a vector balancing in had , this forces With , , and , inequality (4.5) makes the lower endpoint strictly exceed the upper endpoint. The -Lipschitz bound on simultaneously verifies all recursive hypotheses and the asymptotics required for divergence and dimension.
Full paper, version 1 ↗Rate avoidance and attainment
Pages 24–27 · Section 6 · arXiv:2607.24161v1
At , the vertical term for every is at most half of ; hence an -lower envelope forces . For attainment, Lemma 6.2 correctly takes powers of the coordinate bounds and uses and to obtain the common factor . The choice lies strictly between and , so all tree errors are sublinear in the return step and , exactly as required.
Full paper, version 1 ↗03Novelty0 reported findingsNo non-novelty findings
No non-novelty findings.