
namespace CGAL {
/*!

\mainpage User Manual
\anchor Chapter_Triangulations

\cgalAutoToc
\authors Olivier Devillers, Samuel Hornus, and Clément Jamin.

This package proposes data structures and algorithms to compute
triangulations of points in any dimensions \cgalCite{boissonnat2009Delaunay}.
The `Triangulation_data_structure` handles the
combinatorial aspect of triangulations while the geometric classes
`Triangulation`, `Delaunay_triangulation` and `Regular_triangulation` allow to
compute and maintain triangulations, Delaunay triangulations, and
regular triangulations of sets of points.

\section TriangulationSecIntro Introduction

## Some Definitions ##

A <I>finite abstract simplicial complex</I> is built on a finite set of
vertices \f$ V\f$ and consists of a collection \f$ S\f$ of subsets of \f$ V\f$ such that,
if \f$ s\f$ is a set of vertices in \f$ S\f$, then all the subsets of \f$ s\f$ are also
in \f$ S\f$.

The sets in \f$ S\f$ (which are subsets of \f$ V\f$) are called
<I>faces</I> or <I>simplices</I> (the
singular of which is <I>simplex</I>).
A simplex \f$ s\in S\f$ is <I>maximal</I> if it is not a proper subset of some other
set in \f$ S\f$.
A simplex having \f$ k+1 \f$ vertices is said of dimension \f$ k \f$.
An \f$ k\f$-face denotes a \f$ k\f$-dimensional simplex, i.e., a simplex with \f$ k+1\f$
vertices. 
The simplicial complex is <I>pure</I> if all the maximal simplices
have the same dimension.

A <i>triangulation</i> is a simplicial complex 
that is pure, connected and without boundaries nor singularities. The 
<i>dimension</i> of the triangulation is the dimension of its maximal
simplices.

<!--- cardinality, i.e., they have the same number of vertices.--->
In the sequel, we will call these maximal simplices <I>full cells</I>.
A <I>face</I> of a simplex is a subset of this simplex.
A <I>proper face</I> of a simplex is a strict subset of this simplex.
Two faces \f$ \sigma\f$ and \f$ \sigma'\f$ are <I>incident</I> if and only if
\f$ \sigma'\f$ is a proper face of \f$ \sigma\f$ or <I>vice versa</I>.

A complex has <i>no boundaries</i> if any proper face of a simplex is also a 
proper face of another simplex.

If the triangulation is of dimension \f$ d \f$, we use the following terminology:<UL>
<LI><I>face</I>: an \f$ i\f$-face for some \f$ i\in[0,d]\f$;
<LI><I>vertex</I>: a \f$ 0\f$-face;
<LI><I>edge</I>: a \f$ 1\f$-face;
<LI><I>ridge</I>: a \f$ (d-2)\f$-face;
<LI><I>facet</I>: a \f$ (d-1)\f$-face;
<LI><I>full cell</I>: a \f$ d\f$-face.
</UL>

If the vertices are embedded into Euclidean space \f$ \mathbb{R}^n\f$, 
we deal with
<I>finite simplicial complexes</I>, which have slightly different simplices
and additional requirements:
<UL>
<LI>vertices correspond to points in space.
<LI>a simplex \f$ s\in S\f$ is the convex hull of its vertices.
<LI>the vertices of a simplex \f$ s\in S\f$ are affinely independent.
<LI>the intersection of any two simplices of \f$ S\f$ is a proper face of both
simplices (the empty set counts).
</UL>

## What is Provided in this Package? ##

This \cgal package provides four main classes
for creating and manipulating triangulations.

The class `CGAL::Triangulation_data_structure<Dimensionality, TriangulationDSVertex_, TriangulationDSFullCell_>` 
models an <I>abstract triangulation</I>: vertices in this
class are not embedded in Euclidean space but are only of combinatorial
nature.

The class `CGAL::Triangulation<TriangulationTraits_, TriangulationDataStructure_>` 
describes an embedded triangulation that has as vertices a given set of points.
Methods are provided for the insertion of points in the triangulation, the
traversal of various elements of the triangulation, as well as the location of a
query point inside the triangulation. 
The triangulation covers the convex hull of the set of points.

The class `CGAL::Delaunay_triangulation<DelaunayTriangulationTraits_, TriangulationDataStructure_>` 
builds the Delaunay triangulation of a set of points. 
In a Delaunay triangulation, each face has the so-called
<I>Delaunay</I> or <I>empty-ball</I> property: there exists a
circumscribing ball whose interior does not contain 
any vertex of the triangulation.

The class `CGAL::Regular_triangulation<RegularTriangulationTraits_, TriangulationDataStructure_>` 
builds the regular triangulation 
-- also known as weighted Delaunay triangulation -- of a set of points. 
A detailed definition of such a triangulation is available in section
\ref TriangulationSecRT.

\section TriangulationSecTDS Triangulation Data Structure

In this section, we describe the concept `TriangulationDataStructure` for
which \cgal provides one model class:
`CGAL::Triangulation_data_structure<Dimensionality, TriangulationDSVertex_, TriangulationDSFullCell_>`.

A triangulation data structure can represent an abstract triangulation.
 
The <I>maximal dimension</I> of a triangulation data structure is a 
positive integer equal to the maximum dimension a full cell can have.
This maximal dimension can be chosen by the user at the creation of the 
triangulation data structure and can then be obtained using the method `tds.maximal_dimension()`.
A triangulation data structure also knows the <I>current dimension</I> of its full cells,
which can be obtained using `tds.current_dimension()`. In the sequel, let
us denote the maximal dimension with \f$ D \f$ and the current dimension with \f$ d \f$.
The inequalities \f$ -2 \leq d \leq D\f$ and \f$ 0 < D\f$ always hold.
The special meaning of negative values for \f$d\f$ is explained below.

## The Set of Faces ##

The set of faces of a triangulation data structure with 
current dimension \f$ d \f$ forms a triangulation of the 
topological sphere \f$ \mathbb{S}^d\f$.

Two full cells \f$ \sigma\f$ and \f$ \sigma'\f$ sharing a facet are called
<I>neighbors</I>. A full cell has always exactly \f$ d+1\f$ neighbors.

Possible values of \f$d\f$ (the <I>current dimension</I> of the triangulation) include
<BLOCKQUOTE>
<DL>
<DT><B>\f$d=-2\f$</B><DD> This corresponds to an empty
triangulation data structure.
<DT><B>\f$d=-1\f$</B><DD> This corresponds to an abstract simplicial 
complex reduced to a single vertex.
 <!---  and a single full cell. In a geometric triangulation, this vertex corresponds to the vertex at infinity.--->
<DT><B>\f$d=0\f$</B><DD> This corresponds to an abstract simplicial 
complex including two vertices, each corresponding to a full cell;
the two full cells being neighbors of each other. This is the unique
triangulation of the \f$ 0\f$-sphere.
<!--- (geometrically, the finite vertex and the infinite vertex),--->
<DT><B>\f$ 0< d \le D\f$</B><DD> This corresponds to a triangulation of
the sphere \f$ \mathbb{S}^d\f$.
</DL>
</BLOCKQUOTE> 

## The `Triangulation_data_structure` Class ##

We give here some details about the class
`Triangulation_data_structure<Dimensionality, TriangulationDSVertex_, TriangulationDSFullCell_>`
implementing the concept `TriangulationDataStructure`.

### Storage ###

A triangulation data structure explicitly stores its vertices and full cells.

Each vertex stores a reference to one of its incident full cells.

Each full cell \f$ \sigma \f$ stores references to its \f$ d+1\f$ vertices and
neighbors. Its vertices and neighbors are indexed from \f$ 0\f$ to \f$ d \f$. The indices
of its neighbors have the following meaning: the \f$ i\f$-th neighbor of \f$ \sigma\f$
is the unique neighbor of \f$ \sigma\f$ that does not contain the \f$ i\f$-th vertex of
\f$ \sigma\f$; in other words, it is the neighbor of \f$ \sigma\f$ <I>opposite</I> to
the \f$ i\f$-th vertex of \f$ \sigma\f$ (Figure \cgalFigureRef{triangulationfigfullcell}).

The vertices and full cells of the triangulations are accessed through
`handles` and `iterators`. A handle is a model of the
`Handle` concept, and supports the two dereference operators and
`operator->`.

\cgalFigureBegin{triangulationfigfullcell,simplex-structure.png}
Indexing the vertices and neighbors of a full cell \f$ c\f$ in dimension \f$ d=2\f$.
\cgalFigureEnd

Faces of dimension between 0 and \f$ d-1 \f$ can be accessed as
subfaces of a full cell, through the nested type `Face`. The `Face` instance 
corresponding to a face \f$ f \f$ stores a reference to a full cell `c`
containing \f$ f \f$, and the indices of the vertices of `c` that belong
to \f$ f \f$.

\if READY_TO_PUBLISH
\cgalAdvanced The index of a full cell \f$ c\f$ in the \f$ i\f$-th
neighbor of \f$ c\f$ is called the <I>\f$ i\f$-th mirror-index</I> of
\f$ c\f$ (Figure \cgalFigureRef{triangulationfigfullcell}).  Mirror indices are
often needed for maintaining the triangulation data structure. Thus,
it might be desirable, for performance reasons, to store the mirror
indices alongside the references to the vertices and neighbors in a
full cell. This improves speed a little, but requires more memory.

\cgalAdvanced \cgal provides the class template
`Triangulation_ds_full_cell<TriangulationDataStructure_,
TriangulationDSFullCellStoragePolicy>` for representing full cells in a
triangulation. Its second template parameter is used to specify wether
or not the mirror indices should be kept in memory or computed
on-the-fly, which is the default case.  Please refer to the
documentation of that class template for specific details.
\endif

###Template Parameters###

The `Triangulation_data_structure<Dimensionality, TriangulationDSVertex_, TriangulationDSFullCell_>`
class is designed in such a way that its user can choose
<UL>
<LI>the maximal dimension of the triangulation data structure by specifying the `Dimensionality` template parameter,
<LI>the type used to represent vertices by specifying the `TriangulationDSVertex_`
template parameter and
<LI>the type used to represent full cells by specifying the
`TriangulationDSFullCell_` template parameter.
</UL>

The last two parameters have default values and are thus not necessary, unless
the user needs custom types (see `Triangulation_data_structure`). 
The first template parameter, `Dimensionality`, must be one of the following:
<UL>
<LI>`CGAL::Dimension_tag<D>` for some integer \f$ D \f$. This
indicates that the triangulation can store full cells of dimension at most
\f$ D \f$. The maximum dimension \f$ D \f$ is known by the compiler, which
triggers some optimizations.
<LI>`CGAL::Dynamic_dimension_tag`. In this case, the maximum
dimension of the full cells must be passed as an integer argument to an instance
constructor (see `TriangulationDataStructure`).
</UL>

The `TriangulationDSVertex_` and `TriangulationDSFullCell_` parameters to the class template
must be models of the concepts `TriangulationDSVertex` and
`TriangulationDSFullCell` respectively. \cgal provides models for these
concepts: `Triangulation_ds_vertex<TriangulationDataStructure_>` and
`Triangulation_ds_full_cell<TriangulationDataStructure_, TriangulationDSFullCellStoragePolicy>`, which, as one
can see, take the triangulation data structure as a template parameter in order to get access to
some nested types in `TriangulationDataStructure_`.

The default values are `CGAL::Triangulation_ds_vertex<TDS>`
and `CGAL::Triangulation_ds_full_cell<TDS>`
where `TDS` is the current class 
`Triangulation_data_structure<Dimensionality, TriangulationDSVertex_, TriangulationDSFullCell_>`.
<I>This creates a circular dependency</I>, which we resolve in the same way
as in the \cgal `Triangulation_2` and `Triangulation_3` packages (see
Chapters \ref Chapter_2D_Triangulation_Data_Structure, \ref Chapter_2D_Triangulations, 
\ref Chapter_3D_Triangulation_Data_Structure, and \ref Chapter_3D_Triangulations).
In particular, models of the concepts `TriangulationDSVertex` and
`TriangulationDSFullCell` must provide a nested template `Rebind_TDS`
which is documented in those two concept's reference manual pages.
This mechanism can be used to provide a custom vertex or full cell
class. The user  is encouraged to read the documentation of the \cgal
`Triangulation_2` or `Triangulation_3` package.


## Examples ##

### Incremental Construction ###

The following examples shows how to construct a triangulation data structure by
inserting vertices. Its main interest is that it demonstrates most of the API
to insert new vertices into the triangulation. 
<!---
Therefore, the reader will make
the best use of this example by reading it slowly, together with the reference
manual documentation of the methods that are called (see here:
`TriangulationDataStructure`) and by trying to understand the various
`assert(...)` statements.--->

\cgalExample{triangulation_data_structure_static.cpp}

In the previous example, the maximal dimension is fixed at compile time.
It is also possible to fix it at run time, as in the next example.

\cgalExample{triangulation_data_structure_dynamic.cpp}

### Barycentric Subdivision ###

This example provides a function for computing the barycentric subdivision of a
single full cell `c` in a triangulation data structure. The other
full cells adjacent to `c` are automatically subdivided to match the
subdivision of the full cell `c`. The barycentric subdivision of `c` is
obtained by enumerating all the faces of `c` in order of decreasing
dimension, from the dimension of `c` to dimension 1, and inserting a new
vertex in each face.

\cgalFigureBegin{triangulationfigbarycentric,barycentric-subdivision.png}
Barycentric subdivision in dimension \f$ d=2\f$.
\cgalFigureEnd

\cgalExample{barycentric_subdivision.cpp}

\section TriangulationSecTriangulations Triangulations

The class `CGAL::Triangulation<TriangulationTraits_, TriangulationDataStructure_>` 
maintains a triangulation embedded in Euclidean space. The triangulation 
covers the convex hull of the input points (the embedded vertices of the
triangulation).

To store this triangulation in a triangulation data structure, we turn the set
of its faces into a topological sphere by adding a
fictitious vertex, called the <i>infinite vertex</i>, as well as infinite 
simplices incident to boundary faces of the convex hull. 
Each infinite \f$ i\f$-simplex is 
incident to the infinite vertex and to an \f$ (i-1)\f$-simplex of the 
convex hull boundary.

See Chapters \ref Chapter_2D_Triangulations "2D Triangulations" or 
\ref Chapter_3D_Triangulations "3D Triangulations" for more details
about infinite vertices and cells.

Methods are provided for the insertion of points in the triangulation, the
contraction of faces, the traversal of various elements of the triangulation
as well as the location of a query point inside the triangulation.

The ordering of the vertices of a full cell defines an orientation of
that full cell.
As long as no <I>advanced</I> class method is called, it is guaranteed
that all finite full cells have positive orientation. Each infinite full 
cell is oriented as if its infinite vertex was on the side of
the hyperplane supported by its finite facet where there is no other point.

## Implementation ##

The class `CGAL::Triangulation<TriangulationTraits_, TriangulationDataStructure_>` 
stores a model of the concept `TriangulationDataStructure` that is 
instantiated with a vertex type that stores a point.

The template parameter `TriangulationTraits_` must be a model of the concept
`TriangulationTraits`, which provides the point type as well
as various geometric predicates used by the `Triangulation` class.

The `TriangulationTraits` concept includes a nested type 
`TriangulationTraits::Dimension`. This dimension governs the number of points 
given as arguments to the predicates. This type is either
`CGAL::Dimension_tag<D>` or `CGAL::Dynamic_dimension_tag`.
In any case, the dimension of the traits
must match the maximal dimension of the triangulation data structure.

The template parameter `TriangulationDataStructure_` must be a model of the concept
`TriangulationDataStructure` which provides the triangulation data
structure as described in the previous section.

## Examples ##

### Incremental Construction ###

The following example shows how to construct a triangulation in which we insert
random points. In `STEP 1`, we generate one hundred random points in
\f$ \mathbb{R}^5\f$, which we then insert into a triangulation. In `STEP 2`, we
ask the triangulation to construct the set of edges
(\f$ 1\f$ dimensional faces) incident to the vertex at infinity. It is easy to see that
these edges are in bijection with the vertices on the convex hull of the
points. This gives us a handy way to count the convex hull vertices
(include files <tt>triangulation1.cpp</tt> and
<tt>triangulation2.cpp</tt> are given and commented below). 

\cgalExample{triangulation.cpp}

### Traversing the Facets of the Convex Hull ###

Remember that a triangulation covers the convex hull of its
vertices.
Each facet of the convex hull is incident 
to one finite full cell and one infinite
full cell. In fact there is a bijection between the infinite full cells and the
facets of the convex hull.
If vertices are not in general position, convex hull faces that are
not simplices are triangulated.
In order to traverse the convex hull facets,
there are (at least) two possibilities:

The first is to iterate over the full cells of the triangulation and check if they
are infinite or not:

\cgalExample{triangulation1.cpp}

A second possibility is to ask the triangulation to gather all the full cells
incident to the infinite vertex: they form precisely the set of infinite
full cells:


\cgalExample{triangulation2.cpp}

One important difference between the two examples above is that the first uses
<I>little</I> memory but traverses <I>all</I> the full cells, while the second
visits <I>only</I> the infinite full cells but stores handles to them into the
<I>potentially big</I> array <tt>infinite_full_cells</tt>.

\section TriangulationSecDT Delaunay Triangulations

The class `CGAL::Delaunay_triangulation<DelaunayTriangulationTraits_, TriangulationDataStructure_>` derives from
`CGAL::Triangulation<DelaunayTriangulationTraits_, TriangulationDataStructure_>`
and represents Delaunay triangulations.

A <I>circumscribing ball</I> of a simplex is a ball
having all vertices of the simplex on its boundary.
In a Delaunay triangulation, each face has the so-called
<I>Delaunay</I> or <I>empty-ball</I> property: there exists a
circumscribing ball whose interior does not contain 
any vertex of the triangulation.

In case of degeneracies (co-spherical points) the triangulation is not
uniquely defined. Note however that the CGAL implementation computes a 
unique triangulation even in these cases.

When a new point `p` is inserted into a Delaunay triangulation, the
full cells whose circumscribing ball contains `p` are said to
<I>be in conflict</I> with point `p`. Note that the circumscribing ball
of an infinite full cell is the empty half-space bounded by the affine hull 
of the finite facet of this cell. The set of full cells that are in
conflict with `p` form the <I>conflict zone</I>. The full cells
in the conflict zone are removed, leaving a hole that contains `p`. That
hole is ``star shaped'' around `p` and thus is re-triangulated using
`p` as a center vertex.

Delaunay triangulations support insertion of points, removal of vertices,
and location of a query point inside the triangulation.
Note that inserting a large set of points at once is much faster
than inserting the same points one by one.

## Implementation ##

The class `CGAL::Delaunay_triangulation<DelaunayTriangulationTraits_, TriangulationDataStructure_>` derives from
`CGAL::Triangulation<DelaunayTriangulationTraits_, TriangulationDataStructure_>`. It thus stores a model of
the concept `TriangulationDataStructure`, which is instantiated with a vertex
type that stores a geometric point and allows its retrieval.

The template parameter `DelaunayTriangulationTraits_` must be a model of the concept
`DelaunayTriangulationTraits` which provides the geometric `Point` type as
well as various geometric predicates used by the `Delaunay_triangulation` class.
The concept `DelaunayTriangulationTraits` refines the concept
`TriangulationTraits` by requiring a few additional geometric predicates, necessary
for the computation of Delaunay triangulations.

## Examples ##

### Access to the Conflict Zone and to the Full Cells Created during Point Insertion ###

When using a full cell type containing additional custom information, it may be
useful to get an efficient access to the full cells that are going to be erased
upon the insertion of a new point in the Delaunay triangulation, and to the newly
created full cells. The second part of code example below shows how one can have efficient
access to both the conflict zone and the created full cells, while still
retaining an efficient update of the Delaunay triangulation.

\cgalExample{delaunay_triangulation.cpp}

\section TriangulationSecRT Regular Triangulations

The class `CGAL::Regular_triangulation<RegularTriangulationTraits_, TriangulationDataStructure_>` derives from
`CGAL::Triangulation<RegularTriangulationTraits_, TriangulationDataStructure_>`
and represents regular triangulations.

Regular triangulations are also known as weighted Delaunay triangulations.

Let \f$ {S}^{(w)}\f$ be a set of weighted points in \f$ \mathbb{R}^D\f$. Let
\f$ {p}^{(w)}=(p,w_p), p\in\mathbb{R}^D, w_p\in\mathbb{R}\f$ and
\f$ {z}^{(w)}=(z,w_z), z\in\mathbb{R}^D, w_z\in\mathbb{R}\f$ 
be two weighted points.
A weighted point \f$ {p}^{(w)}=(p,w_p)\f$ can also be seen as a sphere of 
center \f$ p\f$ and radius \f$ \sqrt{w_p}\f$.
The <I>power product</I> (or <I>power distance</I> )
between \f$ {p}^{(w)}\f$ and \f$ {z}^{(w)}\f$ is
defined as
\f[ \Pi({p}^{(w)},{z}^{(w)}) = {\|{p-z}\|^2-w_p-w_z} \f]
where \f$ \|{p-z}\|\f$ is the Euclidean distance between \f$ p\f$ and \f$ z\f$.
\f$ {p}^{(w)}\f$ and \f$ {z}^{(w)}\f$
are said to be <I>orthogonal</I> if \f$ \Pi({p}^{(w)},{z}^{(w)})
= 0\f$.

\f$D + 1\f$ weighted points have a unique common orthogonal weighted point 
called the <I>power sphere</I>. A sphere \f$ {z}^{(w)}\f$ is said to be
<I>regular</I> if \f$ \forall {p}^{(w)}\in{S}^{(w)},
\Pi({p}^{(w)},{z}^{(w)})\geq 0\f$.

A triangulation of \f$ {S}^{(w)}\f$ is <I>regular</I> if the power spheres
of all simplices are regular.

Note that as a result, some points can be hidden and do not result in vertices
in the triangulation. Those points are discarded and cannot be retrieved.

A weighted point `p` is said to be in conflict 
with a simplex `s` if it has a negative power distance to the power sphere of `s`.

Regular triangulations support insertion of weighted points, 
and location of a query point inside the triangulation.
Note that inserting a large set of points at once is much faster
than inserting the same points one by one.
\warning The removal of vertices is not supported yet.


## Implementation ##

The class `CGAL::Regular_triangulation<RegularTriangulationTraits_, TriangulationDataStructure_>` derives from
`CGAL::Triangulation<RegularTriangulationTraits_, TriangulationDataStructure_>`. It thus stores a model of
the concept `TriangulationDataStructure_` which is instantiated with a vertex
type that stores a weighted point and allows its retrieval.

The template parameter `RegularTriangulationTraits_` must be a model of the concept
`RegularTriangulationTraits`. It must provide the `%Weighted_point_d` 
type as well as various geometric predicates used by the 
`Regular_triangulation` class.
The concept `RegularTriangulationTraits` refines the concept
`TriangulationTraits`.

## Example ##

This simple example shows how to create a regular triangulation.

\cgalExample{regular_triangulation.cpp}

\section TriangulationSecPerf Complexity and Performances

When inserting a batch of points into a Delaunay triangulation, 
the current implementation starts by spatially sorting the points. 
Then, for each point to insert, it locates it by walking in the triangulation,
using the previously inserted vertex as a "hint". Finally, the point is
inserted.
In the worst case scenario, without spatial sort, the expected complexity is 
\f$ O(n^{\lceil\frac{d}{2}\rceil+1}) \f$.
When the algorithm is run on uniformly distributed points, the localization complexity is
\f$ O(n^{\frac{1}{d}}) \f$ and the size of the triangulation is \f$ O(n) \f$, which gives
a complexity of \f$ O(n^{1+\frac{1}{d}}) \f$ for the insertion.
With spatial sort and random points, one can expect a complexity of \f$ O(n\log n) \f$.
Please refer to \cgalCite{boissonnat2009Delaunay} for more details.

We provide below (\cgalFigureRef{Triangulationfigbenchmarks100}, 
\cgalFigureRef{Triangulationfigbenchmarks1000} and 
\cgalFigureRef{triangulationfigbenchmarkchart}) the
performance of the Delaunay triangulation on randomly distributed points. 
The machine used is a PC running 
Windows 7 64-bits with an Intel Xeon CPU clocked at 2.80 GHz with 32GB of RAM.
The program has been compiled with Microsoft Visual C++ 2013 in Release mode.

\cgalFigureAnchor{Triangulationfigbenchmarks100}
<CENTER>
<TABLE CELLSPACING=15 align=center>
<tr><td ALIGN=LEFT NOWRAP COLSPAN=13><HR></td></tr>
<tr><th ALIGN=RIGHT NOWRAP>Dimension</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th><th>9</th><th>10</th><th>11</th><th>12</th></tr>
<tr><td ALIGN=LEFT NOWRAP COLSPAN=13><HR></td></tr>
<tr align=center><td align=right>Time (s)</td><td>0.003</td><td>0.007</td><td>0.03</td><td>0.14</td><td>0.56</td><td>2.7</td><td>11.3</td><td>45</td><td>185</td><td>686</td><td>2390</td></tr>
<tr align=center><td align=right>Memory (MB)</td><td>< 1</td><td>< 1</td><td>< 1</td><td>1</td><td>3</td><td>13</td><td>53</td><td>182</td><td>662</td><td>2187</td><td>7156</td></tr>
<tr align=center><td align=right>Number of maximal simplices</td><td>184</td><td>487</td><td>1,548</td><td>5,548</td><td>19,598</td><td>67,102</td><td>230,375</td><td>715,984</td><td>2,570,623</td><td>7,293,293</td><td>21,235,615</td></tr>
<tr align=center><td align=right>Number of convex hull facets</td><td>14</td><td>66</td><td>308</td><td>1,164</td><td>4,410</td><td>16,974</td><td>57,589</td><td>238,406</td><td>670,545</td><td>2,574,326</td><td>8,603,589</td></tr></td><td>
<tr><td ALIGN=LEFT NOWRAP COLSPAN=13><HR></td></tr>
</TABLE>
</CENTER>
\cgalFigureCaptionBegin{Triangulationfigbenchmarks100}
Performance of the insertion of 100 points in a Delaunay triangulation.
\cgalFigureCaptionEnd

\cgalFigureAnchor{Triangulationfigbenchmarks1000}
<CENTER>
<TABLE CELLSPACING=15 align=center>
<tr><td ALIGN=LEFT NOWRAP COLSPAN=9><HR></td></tr>
<tr><th ALIGN=RIGHT NOWRAP>Dimension</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th><th>8</th></tr>
<tr><td ALIGN=LEFT NOWRAP COLSPAN=9><HR></td></tr>
<tr align=center><td align=right>Time (s)</td><td>0.01</td><td>0.05</td><td>0.5</td><td>3.4</td><td>24</td><td>183</td><td>1365</td></tr>
<tr align=center><td align=right>Memory (MB)</td><td>< 1</td><td>< 1</td><td>2.7</td><td>14</td><td>81</td><td>483</td><td>2827</td></tr>
<tr align=center><td align=right>Number of maximal simplices</td><td>1,979</td><td>6,315</td><td>25,845</td><td>122,116</td><td>596,927</td><td>3,133,318</td><td>16,403,337</td></tr>
<tr align=center><td align=right>Number of convex hull facets</td><td>19</td><td>138</td><td>963</td><td>6,184</td><td>41,135</td><td>241,540</td><td>1,406,797</td></tr></td><td>
<tr><td ALIGN=LEFT NOWRAP COLSPAN=9><HR></td></tr>
</TABLE>
</CENTER>
\cgalFigureCaptionBegin{Triangulationfigbenchmarks1000}
Performance of the insertion of 1000 points in a Delaunay triangulation.
\cgalFigureCaptionEnd

\cgalFigureBegin{triangulationfigbenchmarkchart,benchmark_DTd.png}
Running time wrt. number of maximal simplices, for dimensions for 2 to 12.
\cgalFigureEnd

\section TriangulationSecDesign Design and Implementation History

Starting with the version 2.3 of \cgal, a package written by Susan Hert and Michael Seel
was the first able to deal with triangulation and convex hulls in arbitrary
dimension. It is deprecated since the version 4.6 of \cgal and this package should be used
instead.

This package is heavily inspired by the works of
Monique Teillaud and Sylvain Pion (`Triangulation_3`)
and Mariette Yvinec (`Triangulation_2`).
The first version was written by Samuel Hornus. The final version is a joint 
work by Samuel Hornus, Olivier Devillers and Clément Jamin. In 2017, Clément
Jamin added the regular triangulations.

Clément Jamin's work was supported by the
<a href="https://cordis.europa.eu/project/rcn/111529_en.html">Advanced Grant of the European Research Council GUDHI</a>
(Geometric Understanding in Higher Dimensions).


*/ 
} /* namespace CGAL */

