Filtered Complexes

## Detailed Description

A simplicial complex $$\mathbf{K}$$ on a set of vertices $$V = \{1, \cdots ,|V|\}$$ is a collection of simplices $$\{\sigma\}$$, $$\sigma \subseteq V$$ such that $$\tau \subseteq \sigma \in \mathbf{K} \rightarrow \tau \in \mathbf{K}$$. The dimension $$n=|\sigma|-1$$ of $$\sigma$$ is its number of elements minus $$1$$.

A filtration of a simplicial complex is a function $$f:\mathbf{K} \rightarrow \mathbb{R}$$ satisfying $$f(\tau)\leq f(\sigma)$$ whenever $$\tau \subseteq \sigma$$. Ordering the simplices by increasing filtration values (breaking ties so as a simplex appears after its subsimplices of same filtration value) provides an indexing scheme.

# Implementations

## Simplex tree

There are two implementation of complexes. The first on is the Simplex_tree data structure. The simplex tree is an efficient and flexible data structure for representing general (filtered) simplicial complexes. The data structure is described in [6]

Simplex tree representation

### Examples

Here is a list of simplex tree examples :

Simplex tree construction with $$\mathbb{Z}/3\mathbb{Z}$$ coefficients on weighted graph Klein bottle file:

\$> ./simplex_tree_from_cliques_of_graph ../../data/points/Klein_bottle_complex.txt 3
Insert the 1-skeleton in the simplex tree in 0.000404 s.
max_dim = 3
Expand the simplex tree in 3.8e-05 s.
Information of the Simplex Tree:
Number of vertices = 10 Number of simplices = 98

## Hasse complex

The second one is the Hasse_complex. The Hasse complex is a data structure representing explicitly all co-dimension 1 incidence relations in a complex. It is consequently faster when accessing the boundary of a simplex, but is less compact and harder to construct from scratch.

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