|  Dimension of a Space | 
In General
  $ Def: A notion of dimension
    is a map d: Top → \(\mathbb N\) ∪ {∞}, such that if
    X ≅ Y then d(X) = d(Y),
    and d(\(\mathbb R\)n)
    = n.
  * Separable metrizable spaces:
    Various possible dimension functions, e.g., covering dimension, small inductive
    dimension, large inductive dimension; The main ones coincide and, for a linear
    space, give the number of elements of a basis.
  * Non-metrizable spaces:
    A satisfactory theory does not exist; Even for compact spaces, only the Lebesgue
    covering dimension is really a theory.
  * Other problems of study:
    Sum theorems.
  @ General references: in Eckmann & Ruelle RMP(85);
    Manin BAMS(06) [rev].
  @ Texts: Hurewicz & Wallman 41 [classic; separable spaces];
    Pears 75 [encyclopedic];
    Engelking 78;
    Nagata 83 [general metric spaces];
    in van Mill 90;
    in Sakai 13.
Covering Dimension
  $ For a topological space:
    The least integer n such that every finite open cover of X has
    an open refinement of order not exceeding n  (infinite if there is no such
    n), i.e., n + 1 is the minimum number of elements of an open cover
    that can be made to overlap.
  * Relationships: For a space with
    both a linear and a topological structure, the two definitions in general agree,
    but there are always pathological cases.
  @ References:
    Pasynkov T&A(08) [subset theorem];
    Georgiou et al T&A(12) [three types of invariants].
Small Inductive Dimension
  $ Def: Defined inductively by
    (1) ind(X) = −1 iff X = Ø;
    (2) ind(X) ≤ n,
      n ∈ \(\mathbb N\), if for all x ∈ X,
      G open neighborhood of x, ∃U ⊂ G
      open, with ind(∂U) ≤ n−1;
    (3) ind(X) = n if ind(X) ≤ n and ind(X)
      > n−1.
  * Special cases: ind(X)
    = 0 iff X = Ø and it has an open and closed topological basis.
Fractal or Capacity Dimension
  > s.a. fractals.
  $ For a (fractal) subset A of
    Euclidean space: If N(ε) is the smallest number of balls
    of radius ε needed to cover A,
dfr(A):= − limε → 0 (ln N(ε) / ln ε) .
Hausdorff Dimension
  > s.a. fractals [Mandelbrot set]; random walk.
  $ Def: For a set A contained in a metric space X,
dH(A):= sup{d | md(A) = ∞} = inf{d | md(A) = 0} ,
    where md is
      the d-dimensional outer measure of A.
  * Relationships:
    In general, dH(A)
    ≤ dfr(A),
    but they often coincide [@ Barnsley].
  @ References: in Adler 81, pp188 ff;
    Urbański T&A(09) [transfinite Hausdorff dimension of a metric space];
    Nicolini & Niedner PRD(11)-a1009 [of a quantum particle path with minimal length].
  > Online resources:
    see Wikipedia page.
Information Dimension > s.a. spacetime topology.
$ Def: If B(r, x)
  is the ball of radius r centered at a point x in phase space,
dinfo(x):= limr → 0 (ln V(B(r, x)) / ln r) ,
    where the measure V is the fraction of time spent by the system in a region
    (if the system is ergodic, this dimension is a.e. independent of x).
  @ References: in Ruelle 89.
Other Definitions and Related Concepts
  > s.a. measure theory; Spectral Dimension.
  * Global dimension of a ring R:
    It is 0 if R is a field, 1 if it is a principal ideal domain.
  @ Correlation dimension:
    Ruelle PRS(90),
    comment Essex & Nerenberg PRS(91),
    and references there.
  @ Graph / discrete space: Evako IJTP(94)gq;
    Nowotny & Requardt JPA(98)ht/97;
    Reid PRD(03)gq/02 [causal set];
    Bell & Dranishnikov T&A(08) [asymtotic dimension];
    Smyth et al DM(10) [local topological dimension];
    Calcagni et al PRD(15)-a1412 [dimensional flow];
    > s.a. graph invariants [lattice, spectral
      dimension, and others]; posets.
  @ Generalizations:
    Van Mill & Pol T&A(04) [splintered spaces];
    Georgiou et al T&A(09) [dimension-like functions].
  @ And physics: Brunner et al NJP(14)-a1401 [dimension of physical systems];
    > s.a.  models of dynamical spacetime; physical systems;
      dimensionality of spacetime.
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