Ultrametric space
An ultrametric space is a set equipped with a metric that satisfies the strong triangle inequality: for all points x, y, z in the space, the distance d(x, z) \leq \max\{d(x, y), d(y, z)\}.[1] This condition strengthens the standard triangle inequality of metric spaces and implies that all triangles are isosceles, with each side no longer than the longest of the other two.[2] Ultrametric spaces exhibit distinctive topological properties, such as the equality of open and closed balls, the nesting of balls (where intersecting balls have one contained in the other), and total disconnectedness, meaning no non-trivial connected subsets exist.[3] These spaces are non-Archimedean and often model hierarchical structures, leading to applications in diverse fields including p-adic analysis, where the p-adic numbers \mathbb{Q}_p form a complete ultrametric field under the p-adic valuation. Notable examples include the discrete metric on any set, where distances are 1 between distinct points and 0 otherwise; the space of sequences with the metric d(a, b) = \rho^n (for $0 < \rho < 1), with n the first differing index; and the Cantor set endowed with an ultrametric derived from its ternary expansion.[3] In biology and statistics, ultrametrics arise in codon spaces and hierarchical clustering methods like ascending hierarchical classification.[2] The concept was introduced by Marc Krasner in 1944, motivated by valuations on fields, with further developments by Jean-Pierre Benzécri in 1972 linking it to statistical analysis.[1] Ultrametric spaces have since influenced areas such as fractal geometry, disordered systems in physics, and optimization in random media.[2]Fundamentals
Definition
An ultrametric space is a set X together with a function d: X \times X \to [0, \infty) that satisfies the following axioms for all x, y, z \in X:- d(x, y) = 0 if and only if x = y,
- d(x, y) = d(y, x),
- d(x, z) \leq \max\{d(x, y), d(y, z)\}.