Asymptotic-Type Dimension Bounds through Combinatorial Approaches
Jing Yu, Xingyu Zhu

TL;DR
This paper introduces a probabilistic framework to establish sharp asymptotic dimension bounds in metric geometry, resolving longstanding questions for manifolds with nonnegative Ricci curvature and extending results to metric-measure spaces.
Contribution
It provides new combinatorial and probabilistic methods to derive sharp asymptotic dimension bounds, including for Riemannian manifolds and metric measure spaces, with applications to geometric analysis.
Findings
Proves that for metric measure spaces with volume doubling constant C_D, asdim_{AN}(X) ≤ ⌊log_2 C_D⌋.
Shows that complete Riemannian manifolds with nonnegative Ricci curvature have asdim ≤ their dimension n.
Extends polynomial growth bounds for asymptotic dimension from graphs to metric-measure spaces.
Abstract
We develop a probabilistic framework for large-scale dimension bounds in metric geometry, based on padded decompositions, randomized ball carving on net graphs, and the Lov\'asz Local Lemma. For metric measure spaces with volume doubling constant , we prove the sharp bound . In particular, if is a complete Riemannian -manifold with , then , thereby settling a question of Papasoglu on manifolds with nonnegative Ricci curvature. We also show that if is proper, volume noncollapsed, and has polynomial volume growth rate , then . Moreover, the corresponding control function can be chosen to have polynomial growth. This extends Papasoglu's sharp…
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