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In [[geometry]], a '''triangulation''' is a subdivision of a geometric object into [[simplex|simplices]]. In particular, in the plane it is a subdivision into [[triangle]]s, hence the nameTriangulation of a three-dimensional volume would involve subdividing it into [[tetrahedra]] ("pyramids" of various shapes and sizes) packed together.  
In [[mathematics]], the '''(formal) complex conjugate''' of a [[complex numbers|complex]] [[vector space]] <math>V\,</math> is the complex vector space <math>\overline V</math> consisting of all formal [[complex conjugate]]s of elements of <math>V\,</math>That is, <math>\overline V</math> is a vector space whose elements are in [[bijection|one-to-one correspondence]] with the elements of <math>V\,</math>:
:<math>\overline V = \{\overline v \mid v \in V\},</math>
with the following rules for [[addition]] and [[scalar multiplication]]:
:<math>\overline v + \overline w = \overline{\,v+w\,}\quad\text{and}\quad\alpha\,\overline v = \overline{\,\overline \alpha \,v\,}.</math>
Here <math>v\,</math> and <math>w\,</math> are vectors in <math>V\,</math>, <math>\alpha\,</math> is a complex number, and <math>\overline\alpha</math> denotes the complex conjugate of <math>\alpha\,</math>.


In most instances, the triangles of a triangulation are required to meet edge-to-edge and vertex-to-vertex.
More concretely, the complex conjugate vector space is the same underlying ''real'' vector space (same set of points, same vector addition and real scalar multiplication) with the conjugate [[linear complex structure]] ''J'' (different multiplication by ''i'').


Different types of triangulation may be defined, depending both on what geometric object is to be subdivided and on how the subdivision is determined.
==Antilinear maps==
* A triangulation ''T'' of <math>\mathbb{R}^{n+1}</math> is a subdivision of <math>\mathbb{R}^{n+1}</math> into (''n''&nbsp;+&nbsp;1)-dimensional simplices such that any two simplices in ''T'' intersect in a common face (a simplex of any lower dimension) or not at all, and any [[bounded set]] in <math>\mathbb{R}^{n+1}</math> intersects only [[finite set|finite]]ly many simplices in ''T''. That is, it is a locally finite [[simplicial complex]] that covers the entire space.
If <math>V\,</math> and <math>W\,</math> are complex vector spaces, a function <math>f\colon V \to W\,</math> is [[antilinear]] if
* A [[point set triangulation]], i.e., a triangulation of a [[discrete space|discrete]] set of points <math>P\subset\mathbb{R}^{n+1}</math>, is a subdivision of the [[convex hull]] of the points into simplices such that any two simplices intersect in a common face or not at all and such that the set of vertices of the simplices coincides with <math>P</math>. Frequently used and studied point set triangulations include the [[Delaunay triangulation]] (for points in general position, the set of simplices that are circumscribed by an open ball that contains no input points) and the [[minimum-weight triangulation]] (the point set triangulation minimizing the sum of the edge lengths).
:<math>f(v+v') = f(v) + f(v')\quad\text{and}\quad f(\alpha v) = \overline\alpha \, f(v)</math>
* In [[cartography]], a [[triangulated irregular network]] is a point set triangulation of a set of two-dimensional points together with elevations for each point. Lifting each point from the plane to its elevated height lifts the triangles of the triangulation into three-dimensional surfaces, which form an approximation of a three-dimensional landform.
for all <math>v,v'\in V\,</math> and <math>\alpha\in\mathbb{C}</math>.
* A [[polygon triangulation]] is a subdivision of a given [[polygon]] into triangles meeting edge-to-edge, again with the property that the set of triangle vertices coincides with the set of vertices of the polygon. Polygon triangulations may be found in [[linear time]] and form the basis of several important geometric algorithms, including a simple solution to the [[art gallery problem]]. The [[constrained Delaunay triangulation]] is an adaptation of the Delaunay triangulation from point sets to polygons or, more generally, to [[planar straight-line graph]]s.
* A [[Surface triangulation|triangulation of a surface]] consists of a net of triangles with points on a given surface covering the surface partly or totally.
* In the [[finite element method]], triangulations are often used as the mesh underlying a computation. In this case, the triangles must form a subdivision of the domain to be simulated, but instead of restricting the vertices to input points, it is allowed to add additional [[Steiner point]]s as vertices. In order to be suitable as finite element meshes, a triangulation must have well-shaped triangles, according to criteria that depend on the details of the finite element simulation; for instance, some methods require that all triangles be right or acute, forming [[nonobtuse mesh]]es. Many meshing techniques are known, including [[Delaunay refinement]] algorithms such as [[Chew's second algorithm]] and [[Ruppert's algorithm]].
* In more general topological spaces, [[Triangulation (topology)|triangulations]] of a space generally refer to simplicial complexes that are [[homeomorphic]] to the space.


The concept of a triangulation may also be generalized somewhat to subdivisions into shapes related to triangles. In particular, a [[pseudotriangulation]] of a point set is a partition of the convex hull of the points into pseudotriangles, polygons that like triangles have exactly three convex vertices. As in point set triangulations, pseudotriangulations are required to have their vertices at the given input points.
One reason to consider the vector space <math>\overline V</math> is that it makes antilinear maps into [[linear map]]s.  Specifically, if <math>f\colon V \to W\,</math> is an antilinear map, then the corresponding map <math>\overline V \to W</math> defined by
:<math>\overline v \mapsto f(v)</math>
is linear. Conversely, any linear map defined on <math>\overline V</math> gives rise to an antilinear map on <math>V\,</math>.


==External links==
One way of thinking about this correspondence is that the map <math>C\colon V \to \overline V</math> defined by
* {{mathworld | urlname = SimplicialComplex | title = Simplicial complex}}
:<math>C(v) = \overline v</math>
* {{mathworld | urlname = Triangulation | title = Triangulation}}
is an antilinear bijection. Thus if <math>f\colon \overline V \to W</math> is linear, then [[Function composition|composition]] <math>f \circ C\colon V \to W\,</math> is antilinear, and ''vice versa''.


[[Category:Triangulation (geometry)| ]]
==Conjugate linear maps==
Any linear map <math>f \colon V \to W\,</math> induces a '''conjugate linear map''' <math>\overline f \colon \overline V \to \overline W</math>, defined by the formula
:<math>\overline f (\overline v) = \overline{\,f(v)\,}.</math>
The conjugate linear map <math>\overline f</math> is linear.  Moreover, the [[identity function|identity map]] on <math>V\,</math> induces the identity map <math>\overline V</math>, and
:<math>\overline f \circ \overline g = \overline{\,f \circ g\,}</math>
for any two linear maps <math>f\,</math> and <math>g\,</math>.  Therefore, the rules <math>V\mapsto \overline V</math> and <math>f\mapsto\overline f</math> define a [[functor]] from the [[category theory|category]] of complex vector spaces to itself.
 
If <math>V\,</math> and  <math>W\,</math> are finite-dimensional and the map  <math>f\,</math> is described by the complex [[matrix (mathematics)|matrix]]  <math>A\,</math> with respect to the [[basis of a vector space|bases]]  <math>\mathcal B</math> of  <math>V\,</math> and  <math>\mathcal C</math> of  <math>W\,</math>, then the map  <math>\overline f</math> is described by the complex conjugate of  <math>A\,</math> with respect to the bases  <math>\overline{\mathcal B}</math> of  <math>\overline V</math> and  <math>\overline{\mathcal C}</math> of  <math>\overline W</math>.
 
==Structure of the conjugate==
The vector spaces <math>V\,</math> and <math>\overline V</math> have the same [[dimension of a vector space|dimension]] over the complex numbers and are therefore [[isomorphism|isomorphic]] as complex vector spaces. However, there is no [[natural isomorphism]] from  <math>V\,</math> to  <math>\overline V</math>.  (The map <math>C\,</math> is not an isomorphism, since it is antilinear.)
 
The double conjugate <math>\overline{\overline V}</math> is naturally isomorphic to <math>V\,</math>, with the isomorphism <math>\overline{\overline V} \to V</math> defined by
:<math>\overline{\overline v} \mapsto v.</math>
Usually the double conjugate of <math>V\,</math> is simply identified with <math>V\,</math>.
 
== Complex conjugate of a Hilbert space ==
Given a [[Hilbert space]] <math>\mathcal{H}</math> (either finite or infinite dimensional), its complex conjugate <math>\overline{\mathcal{H}}</math> is the same vector space as its [[continuous dual space]] <math>\mathcal{H}'</math>.
There is one-to-one antilinear correspondence between continuous linear functionals and vectors.
In other words, any continuous [[linear functional]] on <math>\mathcal{H}</math> is an inner multiplication to some fixed vector, and vice versa.
 
Thus, the complex conjugate to a vector <math>v</math>, particularly in finite dimension case, may be denoted as <math>v^*</math> (v-star, a [[row vector]] which is the [[conjugate transpose]] to a column vector <math>v</math>).
In quantum mechanics, the conjugate to a ''ket&nbsp;vector''&nbsp;<math>|\psi\rangle</math> is denoted as <math>\langle\psi|</math> – a ''bra vector'' (see [[bra–ket notation]]).
 
==See also==
* [[Linear complex structure]]
 
==References==
* Budinich, P. and Trautman, A. ''The Spinorial Chessboard''. Spinger-Verlag, 1988. ISBN 0-387-19078-3. (complex conjugate vector spaces are discussed in section 3.3, pag. 26).
 
[[Category:Linear algebra]]
[[Category:Vectors|Vector space]]

Revision as of 17:28, 13 August 2014

In mathematics, the (formal) complex conjugate of a complex vector space is the complex vector space consisting of all formal complex conjugates of elements of . That is, is a vector space whose elements are in one-to-one correspondence with the elements of :

with the following rules for addition and scalar multiplication:

Here and are vectors in , is a complex number, and denotes the complex conjugate of .

More concretely, the complex conjugate vector space is the same underlying real vector space (same set of points, same vector addition and real scalar multiplication) with the conjugate linear complex structure J (different multiplication by i).

Antilinear maps

If and are complex vector spaces, a function is antilinear if

for all and .

One reason to consider the vector space is that it makes antilinear maps into linear maps. Specifically, if is an antilinear map, then the corresponding map defined by

is linear. Conversely, any linear map defined on gives rise to an antilinear map on .

One way of thinking about this correspondence is that the map defined by

is an antilinear bijection. Thus if is linear, then composition is antilinear, and vice versa.

Conjugate linear maps

Any linear map induces a conjugate linear map , defined by the formula

The conjugate linear map is linear. Moreover, the identity map on induces the identity map , and

for any two linear maps and . Therefore, the rules and define a functor from the category of complex vector spaces to itself.

If and are finite-dimensional and the map is described by the complex matrix with respect to the bases of and of , then the map is described by the complex conjugate of with respect to the bases of and of .

Structure of the conjugate

The vector spaces and have the same dimension over the complex numbers and are therefore isomorphic as complex vector spaces. However, there is no natural isomorphism from to . (The map is not an isomorphism, since it is antilinear.)

The double conjugate is naturally isomorphic to , with the isomorphism defined by

Usually the double conjugate of is simply identified with .

Complex conjugate of a Hilbert space

Given a Hilbert space (either finite or infinite dimensional), its complex conjugate is the same vector space as its continuous dual space . There is one-to-one antilinear correspondence between continuous linear functionals and vectors. In other words, any continuous linear functional on is an inner multiplication to some fixed vector, and vice versa.

Thus, the complex conjugate to a vector , particularly in finite dimension case, may be denoted as (v-star, a row vector which is the conjugate transpose to a column vector ). In quantum mechanics, the conjugate to a ket vector  is denoted as – a bra vector (see bra–ket notation).

See also

References

  • Budinich, P. and Trautman, A. The Spinorial Chessboard. Spinger-Verlag, 1988. ISBN 0-387-19078-3. (complex conjugate vector spaces are discussed in section 3.3, pag. 26).