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{{Short description|Multi-dimensional generalization of triangle}}
{{Short description|Kesetaraan dibawah perubahan basis (aljabar linear)}}
[[Berkas:Simplexes.jpg|al=The four simplexes that can be fully represented in 3D space.|jmpl|271x271px|The four simplexes that can be fully represented in 3D space.]]
In [[geometry]], a '''simplex''' (plural: '''simplexes''' or '''simplices''') is a generalization of the notion of a [[triangle]] or [[tetrahedron]] to arbitrary [[dimensions]]. The simplex is so-named because it represents the simplest possible [[polytope]] in any given dimension. For example,


{{other uses|Similarity (geometry)|Similarity transformation (disambiguation)}}{{Distinguish|Matriks keserupaan}}
* a 0-dimensional simplex is a [[Point (mathematics)|point]],
* a 1-dimensional simplex is a [[line segment]],
* a 2-dimensional simplex is a [[triangle]],
* a 3-dimensional simplex is a [[tetrahedron]], and
* a 4-dimensional simplex is a [[5-cell]].


In [[linear algebra]], two ''n''-by-''n'' [[Matrix (mathematics)|matrices]] {{mvar|A}} and {{mvar|B}} are called '''similar''' if there exists an [[Invertible matrix|invertible]] ''n''-by-''n'' matrix {{mvar|P}} such that<math display="block">B = P^{-1} A P .</math>Similar matrices represent the same [[linear map]] under two (possibly) different [[Basis (linear algebra)|bases]], with {{mvar|P}} being the [[change of basis]] matrix.<ref>{{cite book|last1=Beauregard|first1=Raymond A.|last2=Fraleigh|first2=John B.|year=1973|url=https://archive.org/details/firstcourseinlin0000beau|title=A First Course In Linear Algebra: with Optional Introduction to Groups, Rings, and Fields|location=Boston|publisher=[[Houghton Mifflin Co.]]|isbn=0-395-14017-X|pages=240–243|url-access=registration}}</ref><ref>{{citation|first1=Richard|last1=Bronson|year=1970|lccn=70097490|title=Matrix Methods: An Introduction|publisher=[[Academic Press]]|location=New York|pages=176–178}}</ref>
Specifically, a '''{{mvar|k}}-simplex''' is a {{mvar|k}}-dimensional [[polytope]] that is the [[convex hull]] of its {{math|''k'' + 1}} [[Vertex (geometry)|vertices]]. More formally, suppose the {{math|''k'' + 1}} points <math>u_0, \dots, u_k</math> are [[affinely independent]], which means that the {{mvar|k}} vectors <math>u_1 - u_0,\dots, u_k-u_0</math> are [[linearly independent]]. Then, the simplex determined by them is the set of points<math display="block"> C = \left\{\theta_0 u_0 + \dots +\theta_k u_k ~\Bigg|~ \sum_{i=0}^{k} \theta_i=1 \mbox{ and } \theta_i \ge 0 \mbox{ for } i = 0, \dots, k\right\}.</math>A '''regular simplex'''<ref>{{cite book|last=Elte|first=E.L.|date=2006|title=The Semiregular Polytopes of the Hyperspaces.|publisher=Simon & Schuster|isbn=978-1-4181-7968-7|chapter=IV. five dimensional semiregular polytope|author-link=Emanuel Lodewijk Elte|orig-year=1912}}</ref> is a simplex that is also a [[regular polytope]]. A regular {{mvar|k}}-simplex may be constructed from a regular {{math|(''k'' − 1)}}-simplex by connecting a new vertex to all original vertices by the common edge length.


A transformation {{math|''A'' ↦ ''P''<sup>−1</sup>''AP''}} is called a '''similarity transformation''' or '''conjugation''' of the matrix {{mvar|A}}. In the [[general linear group]], similarity is therefore the same as '''[[Conjugacy class|conjugacy]]''', and similar matrices are also called '''conjugate'''; however, in a given subgroup {{mvar|H}} of the general linear group, the notion of conjugacy may be more restrictive than similarity, since it requires that {{mvar|P}} be chosen to lie in {{mvar|H}}.
The '''standard simplex''' or '''probability simplex'''<ref name="Boyd">{{harvnb|Boyd|Vandenberghe|2004}}</ref> is the {{math|(''k'' − 1)}}-dimensional simplex whose vertices are the {{mvar|k}} standard [[unit vectors]] in <math>\mathbf{R}^k</math>, or in other words<math display="block">\left\{x \in \mathbf{R}^{k} : x_0 + \dots + x_{k-1} = 1, x_i \ge 0 \text{ for } i = 0, \dots, k-1 \right\}.</math>In [[topology]] and [[combinatorics]], it is common to "glue together" simplices to form a [[simplicial complex]]. The associated combinatorial structure is called an [[abstract simplicial complex]], in which context the word "simplex" simply means any [[finite set]] of vertices.


== History ==
== Motivating example ==
When defining a linear transformation, it can be the case that a change of basis can result in a simpler form of the same transformation. For example, the matrix representing a rotation in {{math|'''R'''<sup>3</sup>}} when the [[Axis–angle representation|axis of rotation]] is not aligned with the coordinate axis can be complicated to compute. If the axis of rotation were aligned with the positive {{mvar|z}}-axis, then it would simply be<math display="block">S = \begin{bmatrix}
The concept of a simplex was known to [[William Kingdon Clifford]], who wrote about these shapes in 1886 but called them "prime confines". [[Henri Poincaré]], writing about [[algebraic topology]] in 1900, called them "generalized tetrahedra". In 1902 [[Pieter Hendrik Schoute]] described the concept first with the [[Latin]] superlative ''simplicissimum'' ("simplest") and then with the same Latin adjective in the normal form ''simplex'' ("simple").<ref>{{citation|url=http://jeff560.tripod.com/s.html|title=Simplex|work=Earliest Known Uses of Some of the Words of Mathematics|first=Jeff|last=Miller|access-date=2018-01-08}}</ref>
\cos\theta & -\sin\theta & 0 \\
\sin\theta & \cos\theta & 0 \\
0 & 0 & 1
\end{bmatrix},</math>where <math>\theta</math> is the angle of rotation. In the new coordinate system, the transformation would be written as<math display="block">y' = Sx',</math>where {{mvar|x'}} and {{mvar|y'}} are respectively the original and transformed vectors in a new basis containing a vector parallel to the axis of rotation. In the original basis, the transform would be written as<math display="block">y = Tx,</math>where vectors {{mvar|x}} and {{mvar|y}} and the unknown transform matrix {{mvar|T}} are in the original basis. To write {{mvar|T}} in terms of the simpler matrix, we use the change-of-basis matrix {{mvar|P}} that transforms {{mvar|x}} and {{mvar|y}} as <math>x' = Px</math> and <math>y' = Py</math>:<math display="block">\begin{align}
& & y' &= S x' \\[1.6ex]
&\Rightarrow & P y &= S P x \\[1.6ex]
&\Rightarrow & y &= \left(P^{-1} S P\right) x = T x
\end{align}</math>Thus, the matrix in the original basis, <math>T</math>, is given by <math>T = P^{-1}SP</math>. The transform in the original basis is found to be the product of three easy-to-derive matrices. In effect, the similarity transform operates in three steps: change to a new basis ({{mvar|P}}), perform the simple transformation ({{mvar|S}}), and change back to the old basis ({{math|''P''<sup>−1</sup>}}).


== Properties ==
The '''regular simplex''' family is the first of three [[regular polytope]] families, labeled by [[Donald Coxeter]] as {{math|''α''<sub>''n''</sub>}}, the other two being the [[cross-polytope]] family, labeled as {{math|''β''<sub>''n''</sub>}}, and the [[Hypercube|hypercubes]], labeled as {{math|''γ''<sub>''n''</sub>}}. A fourth family, the [[Hypercubic honeycomb|tessellation of {{mvar|n}}-dimensional space by infinitely many hypercubes]], he labeled as {{math|''δ''<sub>''n''</sub>}}.{{sfn|Coxeter|1973|loc=§7.2|pp=120-124}}
Similarity is an [[equivalence relation]] on the space of square matrices.


Because matrices are similar if and only if they represent the same linear operator with respect to (possibly) different bases, similar matrices share all properties of their shared underlying operator:
== Elements ==<!-- This section is linked from [[Simplicial complex]] -->
The [[convex hull]] of any [[Empty set|nonempty]] [[subset]] of the {{math|''n'' + 1}} points that define an {{mvar|n}}-simplex is called a '''face''' of the simplex. Faces are simplices themselves. In particular, the convex hull of a subset of size {{math|''m'' + 1}} (of the {{math|''n'' + 1}} defining points) is an {{mvar|m}}-simplex, called an '''{{mvar|m}}-face''' of the {{mvar|n}}-simplex. The 0-faces (i.e., the defining points themselves as sets of size 1) are called the '''vertices''' (singular: vertex), the 1-faces are called the '''edges''', the ({{math|''n'' − 1}})-faces are called the '''facets''', and the sole {{mvar|n}}-face is the whole {{mvar|n}}-simplex itself. In general, the number of {{mvar|m}}-faces is equal to the [[binomial coefficient]] <math>\tbinom{n+1}{m+1}</math>.{{Sfn|Coxeter|1973|p=120}} Consequently, the number of {{mvar|m}}-faces of an {{mvar|n}}-simplex may be found in column ({{math|''m'' + 1}}) of row ({{math|''n'' + 1}}) of [[Pascal's triangle]]. A simplex {{mvar|A}} is a '''coface''' of a simplex {{mvar|B}} if {{mvar|B}} is a face of {{mvar|A}}. ''Face'' and ''facet'' can have different meanings when describing types of simplices in a [[Simplicial complex#Definitions|simplicial complex]].


* [[Rank (aljabar linear)|Rank]]
The extended [[f-vector]] for an {{mvar|n}}-simplex can be computed by {{math|('''1''','''1''')<sup>''n''+1</sup>}}, like the coefficients of [[Polynomial#Multiplication|polynomial products]]. For example, a [[7-simplex]] is ('''1''','''1''')<sup>8</sup> = ('''1''',2,'''1''')<sup>4</sup> = ('''1''',4,6,4,'''1''')<sup>2</sup> = ('''1''',8,28,56,70,56,28,8,'''1''').
* [[Polinomial karakteristik]], and attributes that can be derived from it:
** [[Determinan]]
** [[Teras (aljabar linear)|Teras]]
** [[Nilai dan vektor eigen|Nilai-nilai eigen]], and their [[Algebraic multiplicity|algebraic multiplicities]]
* [[Geometric multiplicity|Geometric multiplicities]] of eigenvalues (but not the eigenspaces, which are transformed according to the base change matrix ''P'' used).
* [[Minimal polynomial (linear algebra)|Minimal polynomial]]
* [[Frobenius normal form]]
* [[Jordan normal form]], up to a permutation of the Jordan blocks
* [[Nilpotent matrix|Index of nilpotence]]
* [[Elementary divisors]], which form a complete set of invariants for similarity of matrices over a [[principal ideal domain]]


Because of this, for a given matrix ''A'', one is interested in finding a simple "normal form" ''B'' which is similar to ''A''—the study of ''A'' then reduces to the study of the simpler matrix ''B''.
The number of 1-faces (edges) of the {{mvar|n}}-simplex is the {{mvar|n}}-th [[triangle number]], the number of 2-faces of the {{mvar|n}}-simplex is the {{math|(''n'' − 1)}}th [[tetrahedron number]], the number of 3-faces of the {{mvar|n}}-simplex is the {{math|(''n'' − 2)}}th 5-cell number, and so on.
{| class="wikitable"
|+{{mvar|n}}-Simplex elements<ref>{{Cite OEIS|sequencenumber=A135278|name=Pascal's triangle with its left-hand edge removed}}</ref>
!{{math|Δ<sup>''n''</sup>}}
!Name
![[Schläfli symbol|Schläfli]]
[[Coxeter–Dynkin diagram|Coxeter]]
!0-
faces
<small>(vertices)</small>
!1-
faces
<small>(edges)</small>
!2-
faces
<small>(faces)</small>
!3-
faces
<small>(cells)</small>
!4-
faces
<small>&nbsp;</small>
!5-
faces
<small>&nbsp;</small>
!6-
faces
<small>&nbsp;</small>
!7-
faces
<small>&nbsp;</small>
!8-
faces
<small>&nbsp;</small>
!9-
faces
<small>&nbsp;</small>
!10-
faces
<small>&nbsp;</small>
!'''Sum'''
= 2<sup>''n''+1</sup>&nbsp;− 1
|-
!Δ<sup>0</sup>
|0-simplex
([[Vertex (geometry)|point]])
|( )
{{CDD|node}}
|1
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|'''1'''
|-
!Δ<sup>1</sup>
|1-simplex
([[Edge (geometry)|line segment]])
|{&nbsp;} = (&nbsp;) ∨ (&nbsp;) = 2⋅(&nbsp;)
{{CDD|node_1}}
|2
|1
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|'''3'''
|-
!Δ<sup>2</sup>
|2-simplex
([[triangle]])
|{3} = 3⋅(&nbsp;)
{{CDD|node_1|3|node}}
|3
|3
|1
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|'''7'''
|-
!Δ<sup>3</sup>
|3-simplex
([[tetrahedron]])
|{3,3} = 4⋅(&nbsp;)
{{CDD|node_1|3|node|3|node}}
|4
|6
|4
|1
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|'''15'''
|-
!Δ<sup>4</sup>
|4-simplex
([[5-cell]])
|{3<sup>3</sup>} = 5⋅(&nbsp;)
{{CDD|node_1|3|node|3|node|3|node}}
|5
|10
|10
|5
|1
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|'''31'''
|-
!Δ<sup>5</sup>
|[[5-simplex]]
|{3<sup>4</sup>} = 6⋅(&nbsp;)
{{CDD|node_1|3|node|3|node|3|node|3|node}}
|6
|15
|20
|15
|6
|1
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|'''63'''
|-
!Δ<sup>6</sup>
|[[6-simplex]]
|{3<sup>5</sup>} = 7⋅(&nbsp;)
{{CDD|node_1|3|node|3|node|3|node|3|node|3|node}}
|7
|21
|35
|35
|21
|7
|1
|&nbsp;
|&nbsp;
|&nbsp;
|&nbsp;
|'''127'''
|-
!Δ<sup>7</sup>
|[[7-simplex]]
|{3<sup>6</sup>} = 8⋅(&nbsp;)
{{CDD|node_1|3|node|3|node|3|node|3|node|3|node|3|node}}
|8
|28
|56
|70
|56
|28
|8
|1
|&nbsp;
|&nbsp;
|&nbsp;
|'''255'''
|-
!Δ<sup>8</sup>
|[[8-simplex]]
|{3<sup>7</sup>} = 9⋅(&nbsp;)
{{CDD|node_1|3|node|3|node|3|node|3|node|3|node|3|node|3|node}}
|9
|36
|84
|126
|126
|84
|36
|9
|1
|&nbsp;
|&nbsp;
|'''511'''
|-
!Δ<sup>9</sup>
|[[9-simplex]]
|{3<sup>8</sup>} = 10⋅(&nbsp;)
{{CDD|node_1|3|node|3|node|3|node|3|node|3|node|3|node|3|node|3|node}}
|10
|45
|120
|210
|252
|210
|120
|45
|10
|1
|&nbsp;
|'''1023'''
|-
!Δ<sup>10</sup>
|[[10-simplex]]
|{3<sup>9</sup>} = 11⋅(&nbsp;)
{{CDD|node_1|3|node|3|node|3|node|3|node|3|node|3|node|3|node|3|node|3|node}}
|11
|55
|165
|330
|462
|462
|330
|165
|55
|11
|1
|'''2047'''
|}
An {{mvar|n}}-simplex is the [[polytope]] with the fewest vertices that requires {{mvar|n}} dimensions. Consider a line segment ''AB'' as a shape in a 1-dimensional space (the 1-dimensional space is the line in which the segment lies). One can place a new point {{mvar|C}} somewhere off the line. The new shape, triangle ''ABC'', requires two dimensions; it cannot fit in the original 1-dimensional space. The triangle is the 2-simplex, a simple shape that requires two dimensions. Consider a triangle ''ABC'', a shape in a 2-dimensional space (the plane in which the triangle resides). One can place a new point {{mvar|D}} somewhere off the plane. The new shape, tetrahedron ''ABCD'', requires three dimensions; it cannot fit in the original 2-dimensional space. The tetrahedron is the 3-simplex, a simple shape that requires three dimensions. Consider tetrahedron ''ABCD'', a shape in a 3-dimensional space (the 3-space in which the tetrahedron lies). One can place a new point {{mvar|E}} somewhere outside the 3-space. The new shape ''ABCDE'', called a 5-cell, requires four dimensions and is called the 4-simplex; it cannot fit in the original 3-dimensional space. (It also cannot be visualized easily.) This idea can be generalized, that is, adding a single new point outside the currently occupied space, which requires going to the next higher dimension to hold the new shape. This idea can also be worked backward: the line segment we started with is a simple shape that requires a 1-dimensional space to hold it; the line segment is the 1-simplex. The line segment itself was formed by starting with a single point in 0-dimensional space (this initial point is the 0-simplex) and adding a second point, which required the increase to 1-dimensional space.


Similarity of matrices does not depend on the base field: if ''L'' is a field containing ''K'' as a [[Field extension|subfield]], and ''A'' and ''B'' are two matrices over ''K'', then ''A'' and ''B'' are similar as matrices over ''K'' [[if and only if]] they are similar as matrices over ''L''. This is so because the rational canonical form over ''K'' is also the rational canonical form over ''L''. This means that one may use Jordan forms that only exist over a larger field to determine whether the given matrices are similar.
More formally, an {{math|(''n'' + 1)}}-simplex can be constructed as a join (∨ operator) of an {{mvar|n}}-simplex and a point,&nbsp;{{math|( )}}. An {{math|(''m'' + ''n'' + 1)}}-simplex can be constructed as a join of an {{mvar|m}}-simplex and an {{mvar|n}}-simplex. The two simplices are oriented to be completely normal from each other, with translation in a direction orthogonal to both of them. A 1-simplex is the join of two points: {{math|1=( ) ∨ ( ) = 2 ⋅ ( )}}. A general 2-simplex (scalene triangle) is the join of three points: {{math|( ) ∨ ( ) ∨ ( )}}. An [[isosceles triangle]] is the join of a 1-simplex and a point: {{math|{{mset| }} ∨ ( )}}. An [[equilateral triangle]] is 3 ⋅ (&nbsp;) or&nbsp;{3}. A general 3-simplex is the join of 4 points: {{math|( ) ∨ ( ) ∨ ( ) ∨ ( )}}. A 3-simplex with mirror symmetry can be expressed as the join of an edge and two points: {{math|{{mset| }} ∨ ( ) ∨ ( )}}. A 3-simplex with triangular symmetry can be expressed as the join of an equilateral triangle and 1 point: {{math|3.( )∨( )}} or {{math|{{mset|3}}∨( )}}. A [[regular tetrahedron]] is {{math|4 ⋅ ( )}} or {{mset|3,3}} and so on.
{|
|[[Berkas:Pascal's_triangle_5.svg|jmpl|300x300px|The numbers of faces in the above table are the same as in [[Pascal's triangle]], without the left diagonal.]]
|-
|[[Berkas:Tesseract_tetrahedron_shadow_matrices.svg|jmpl|395x395px|The total number of faces is always a [[power of two]] minus one. This figure (a projection of the [[tesseract]]) shows the centroids of the 15 faces of the tetrahedron.]]
|}
In some conventions,<ref>Kozlov, Dimitry, ''Combinatorial Algebraic Topology'', 2008, Springer-Verlag (Series: Algorithms and Computation in Mathematics)</ref> the empty set is defined to be a (−1)-simplex. The definition of the simplex above still makes sense if {{math|1=''n'' = −1}}. This convention is more common in applications to algebraic topology (such as [[simplicial homology]]) than to the study of polytopes.{{clear}}


In the definition of similarity, if the matrix ''P'' can be chosen to be a [[matriks permutasi]] then ''A'' and ''B'' are '''permutation-similar;''' if ''P'' can be chosen to be a [[unitary matrix]] then ''A'' and ''B'' are '''unitarily equivalent.''' The [[spectral theorem]] says that every [[normal matrix]] is unitarily equivalent to some diagonal matrix. [[Specht's theorem]] states that two matrices are unitarily equivalent if and only if they satisfy certain trace equalities.
== Symmetric graphs of regular simplices ==
These [[Petrie polygon|Petrie polygons]] (skew orthogonal projections) show all the vertices of the regular simplex on a [[circle]], and all vertex pairs connected by edges.
{| class="wikitable"
|[[Berkas:1-simplex_t0.svg|100x100px]]
[[Line segment|1]]
|[[Berkas:2-simplex_t0.svg|100x100px]]
[[Triangle|2]]
|[[Berkas:3-simplex_t0.svg|100x100px]]
[[Tetrahedron|3]]
|[[Berkas:4-simplex_t0.svg|100x100px]]
[[5-cell|4]]
|[[Berkas:5-simplex_t0.svg|100x100px]]
[[5-simplex|5]]
|- align="center"
|[[Berkas:6-simplex_t0.svg|100x100px]]
[[6-simplex|6]]
|[[Berkas:7-simplex_t0.svg|100x100px]]
[[7-simplex|7]]
|[[Berkas:8-simplex_t0.svg|100x100px]]
[[8-simplex|8]]
|[[Berkas:9-simplex_t0.svg|100x100px]]
[[9-simplex|9]]
|[[Berkas:10-simplex_t0.svg|100x100px]]
[[10-simplex|10]]
|- align="center"
|[[Berkas:11-simplex_t0.svg|100x100px]]
[[11-simplex|11]]
|[[Berkas:12-simplex_t0.svg|100x100px]]
[[12-simplex|12]]
|[[Berkas:13-simplex_t0.svg|100x100px]]
[[13-simplex|13]]
|[[Berkas:14-simplex_t0.svg|100x100px]]
[[14-simplex|14]]
|[[Berkas:15-simplex_t0.svg|100x100px]]
[[15-simplex|15]]
|- align="center"
|[[Berkas:16-simplex_t0.svg|100x100px]]
[[16-simplex|16]]
|[[Berkas:17-simplex_t0.svg|100x100px]]
[[17-simplex|17]]
|[[Berkas:18-simplex_t0.svg|100x100px]]
[[18-simplex|18]]
|[[Berkas:19-simplex_t0.svg|100x100px]]
[[19-simplex|19]]
|[[Berkas:20-simplex_t0.svg|100x100px]]
[[20-simplex|20]]
|}


== Standard simplex ==
== Lihat pula ==
[[Berkas:2D-simplex.svg|ka|jmpl|214x214px|The standard {{nowrap|2-simplex}} in {{math|'''R'''<sup>3</sup>}}]]
The '''standard {{mvar|n}}-simplex''' (or '''unit {{mvar|n}}-simplex''') is the subset of {{math|'''R'''<sup>''n''+1</sup>}} given by


* [[Canonical form#Linear algebra|Canonical forms]]
: <math>\Delta^n = \left\{(t_0,\dots,t_n)\in\mathbf{R}^{n+1} ~\Bigg|~ \sum_{i = 0}^n t_i = 1 \text{ and } t_i \ge 0 \text{ for } i = 0, \ldots, n\right\}</math>
* [[Matrix congruence]]
* [[Matrix equivalence]]


== Referensi ==
The simplex {{math|Δ<sup>''n''</sup>}} lies in the [[affine hyperplane]] obtained by removing the restriction {{math|''t''<sub>''i''</sub> ≥ 0}} in the above definition.


=== Kutipan ===
The {{math|''n'' + 1}} vertices of the standard {{mvar|n}}-simplex are the points {{math|''e''<sub>''i''</sub> ∈ '''R'''<sup>''n''+1</sup>}}, where
{{reflist}}


=== Pustaka ===
: {{math|1=''e''<sub>0</sub> = (1, 0, 0, ..., 0),}}
{{refbegin}}
: {{math|1=''e''<sub>1</sub> = (0, 1, 0, ..., 0),}}
* {{cite book|last1=Horn|first1=Roger A.|last2=Johnson|first2=Charles R.|year=1985|title=Matrix Analysis|publisher=Cambridge University Press|isbn=0-521-38632-2}} (Similarity is discussed many places, starting at page 44.)
: ⋮
{{refend}}
: {{math|1=''e''<sub>''n''</sub> = (0, 0, 0, ..., 1)}}.

A ''standard simplex'' is an example of a [[0/1-polytope]], with all coordinates as 0 or 1. It can also be seen one [[Facet (geometry)|facet]] of a regular {{math|(''n'' + 1)}}-[[orthoplex]].

There is a canonical map from the standard {{mvar|n}}-simplex to an arbitrary {{mvar|n}}-simplex with vertices ({{math|''v''<sub>0</sub>}}, ..., {{math|''v''<sub>''n''</sub>}}) given by

: <math>(t_0,\ldots,t_n) \mapsto \sum_{i = 0}^n t_i v_i</math>

The coefficients {{math|''t''<sub>''i''</sub>}} are called the [[Barycentric coordinates (mathematics)|barycentric coordinates]] of a point in the {{mvar|n}}-simplex. Such a general simplex is often called an '''affine {{mvar|n}}-simplex''', to emphasize that the canonical map is an [[affine transformation]]. It is also sometimes called an '''oriented affine {{mvar|n}}-simplex''' to emphasize that the canonical map may be [[Orientation (vector space)|orientation preserving]] or reversing.

More generally, there is a canonical map from the standard <math>(n-1)</math>-simplex (with {{mvar|n}} vertices) onto any [[polytope]] with {{mvar|n}} vertices, given by the same equation (modifying indexing):

: <math>(t_1,\ldots,t_n) \mapsto \sum_{i = 1}^n t_i v_i</math>

These are known as [[generalized barycentric coordinates]], and express every polytope as the ''image'' of a simplex: <math>\Delta^{n-1} \twoheadrightarrow P.</math>

A commonly used function from {{math|'''R'''<sup>''n''</sup>}} to the interior of the standard <math>(n-1)</math>-simplex is the [[softmax function]], or normalized exponential function; this generalizes the [[standard logistic function]].

=== Examples ===

* Δ<sup>0</sup> is the point {{math|1}} in {{math|'''R'''<sup>1</sup>}}.
* Δ<sup>1</sup> is the line segment joining {{math|(1, 0)}} and {{math|(0, 1)}} in {{math|'''R'''<sup>2</sup>}}.
* Δ<sup>2</sup> is the [[equilateral triangle]] with vertices {{math|(1, 0, 0)}}, {{math|(0, 1, 0)}} and {{math|(0, 0, 1)}} in {{math|'''R'''<sup>3</sup>}}.
* Δ<sup>3</sup> is the [[regular tetrahedron]] with vertices {{math|(1, 0, 0, 0)}}, {{math|(0, 1, 0, 0)}}, {{math|(0, 0, 1, 0)}} and {{math|(0, 0, 0, 1)}} in {{math|'''R'''<sup>4</sup>}}.
* Δ<sup>4</sup> is the regular [[5-cell]] with vertices {{math|(1, 0, 0, 0, 0)}}, {{math|(0, 1, 0, 0, 0)}}, {{math|(0, 0, 1, 0, 0)}}, {{math|(0, 0, 0, 1, 0)}} and {{math|(0, 0, 0, 0, 1)}} in {{math|'''R'''<sup>5</sup>}}.

=== Increasing coordinates ===
An alternative coordinate system is given by taking the [[indefinite sum]]:

: <math>
\begin{align}
s_0 &= 0\\
s_1 &= s_0 + t_0 = t_0\\
s_2 &= s_1 + t_1 = t_0 + t_1\\
s_3 &= s_2 + t_2 = t_0 + t_1 + t_2\\
&\;\;\vdots\\
s_n &= s_{n-1} + t_{n-1} = t_0 + t_1 + \cdots + t_{n-1}\\
s_{n+1} &= s_n + t_n = t_0 + t_1 + \cdots + t_n = 1
\end{align}
</math>

This yields the alternative presentation by ''order,'' namely as nondecreasing {{mvar|n}}-tuples between 0 and 1:

: <math>\Delta_*^n = \left\{(s_1,\ldots,s_n)\in\mathbf{R}^n\mid 0 = s_0 \leq s_1 \leq s_2 \leq \dots \leq s_n \leq s_{n+1} = 1 \right\}. </math>

Geometrically, this is an {{mvar|n}}-dimensional subset of <math>\mathbf{R}^n</math> (maximal dimension, codimension 0) rather than of <math>\mathbf{R}^{n+1}</math> (codimension 1). The facets, which on the standard simplex correspond to one coordinate vanishing, <math>t_i=0,</math> here correspond to successive coordinates being equal, <math>s_i=s_{i+1},</math> while the [[Interior (topology)|interior]] corresponds to the inequalities becoming ''strict'' (increasing sequences).

A key distinction between these presentations is the behavior under permuting coordinates – the standard simplex is stabilized by permuting coordinates, while permuting elements of the "ordered simplex" do not leave it invariant, as permuting an ordered sequence generally makes it unordered. Indeed, the ordered simplex is a (closed) [[fundamental domain]] for the [[Group action|action]] of the [[symmetric group]] on the {{mvar|n}}-cube, meaning that the orbit of the ordered simplex under the {{mvar|n}}! elements of the symmetric group divides the {{mvar|n}}-cube into <math>n!</math> mostly disjoint simplices (disjoint except for boundaries), showing that this simplex has volume {{math|1/''n''!}}. Alternatively, the volume can be computed by an iterated integral, whose successive integrands are 1, {{mvar|x}}, {{math|''x''<sup>2</sup>/2}}, {{math|''x''<sup>3</sup>/3!}}, ..., {{math|''x''<sup>''n''</sup>/''n''!}}.

A further property of this presentation is that it uses the order but not addition, and thus can be defined in any dimension over any ordered set, and for example can be used to define an infinite-dimensional simplex without issues of convergence of sums.

=== Projection onto the standard simplex ===
Especially in numerical applications of [[probability theory]] a [[Graphical projection|projection]] onto the standard simplex is of interest. Given <math> (p_i)_i</math> with possibly negative entries, the closest point <math>\left(t_i\right)_i</math> on the simplex has coordinates

: <math>t_i= \max\{p_i+\Delta\, ,0\},</math>

where <math>\Delta</math> is chosen such that <math display="inline">\sum_i\max\{p_i+\Delta\, ,0\}=1.</math>

<math>\Delta</math> can be easily calculated from sorting {{math|''p''<sub>''i''</sub>}}.<ref>{{cite arXiv|eprint=1101.6081|title=Projection Onto A Simplex|author=Yunmei Chen|author2=Xiaojing Ye|year=2011|class=math.OC}}</ref> The sorting approach takes <math>O( n \log n)</math> complexity, which can be improved to {{math|O(''n'')}} complexity via [[Selection algorithm|median-finding]] algorithms.<ref>{{Cite journal|last1=MacUlan|first1=N.|last2=De Paula|first2=G. G.|year=1989|title=A linear-time median-finding algorithm for projecting a vector on the simplex of n|journal=Operations Research Letters|volume=8|issue=4|pages=219|doi=10.1016/0167-6377(89)90064-3}}</ref> Projecting onto the simplex is computationally similar to projecting onto the <math>\ell_1</math> ball.

=== Corner of cube ===
Finally, a simple variant is to replace "summing to 1" with "summing to at most 1"; this raises the dimension by 1, so to simplify notation, the indexing changes:

: <math>\Delta_c^n = \left\{(t_1,\ldots,t_n)\in\mathbf{R}^n ~\Bigg|~ \sum_{i = 1}^n t_i \leq 1 \text{ and } t_i \ge 0 \text{ for all } i \right\}.</math>

This yields an {{mvar|n}}-simplex as a corner of the {{mvar|n}}-cube, and is a standard orthogonal simplex. This is the simplex used in the [[simplex method]], which is based at the origin, and locally models a vertex on a polytope with {{mvar|n}} facets.

== Cartesian coordinates for a regular {{mvar|n}}-dimensional simplex in '''R'''<sup>''n''</sup> ==
One way to write down a regular {{mvar|n}}-simplex in {{math|'''R'''<sup>''n''</sup>}} is to choose two points to be the first two vertices, choose a third point to make an equilateral triangle, choose a fourth point to make a regular tetrahedron, and so on. Each step requires satisfying equations that ensure that each newly chosen vertex, together with the previously chosen vertices, forms a regular simplex. There are several sets of equations that can be written down and used for this purpose. These include the equality of all the distances between vertices; the equality of all the distances from vertices to the center of the simplex; the fact that the angle subtended through the new vertex by any two previously chosen vertices is <math>\pi/3</math>; and the fact that the angle subtended through the center of the simplex by any two vertices is <math>\arccos(-1/n)</math>.

It is also possible to directly write down a particular regular {{mvar|n}}-simplex in {{math|'''R'''<sup>''n''</sup>}} which can then be translated, rotated, and scaled as desired. One way to do this is as follows. Denote the [[Basis (linear algebra)|basis vectors]] of {{math|'''R'''<sup>''n''</sup>}} by {{math|'''e'''<sub>1</sub>}} through {{math|'''e'''<sub>''n''</sub>}}. Begin with the standard {{math|(''n'' − 1)}}-simplex which is the convex hull of the basis vectors. By adding an additional vertex, these become a face of a regular {{mvar|n}}-simplex. The additional vertex must lie on the line perpendicular to the barycenter of the standard simplex, so it has the form {{math|(''α''/''n'', ..., ''α''/''n'')}} for some [[real number]] {{mvar|α}}. Since the squared distance between two basis vectors is 2, in order for the additional vertex to form a regular {{mvar|n}}-simplex, the squared distance between it and any of the basis vectors must also be 2. This yields a [[quadratic equation]] for {{mvar|α}}. Solving this equation shows that there are two choices for the additional vertex:

: <math>\frac{1}{n} \left(1 \pm \sqrt{n + 1} \right) \cdot (1, \dots, 1).</math>

Either of these, together with the standard basis vectors, yields a regular {{mvar|n}}-simplex.

The above regular {{mvar|n}}-simplex is not centered on the origin. It can be translated to the origin by subtracting the mean of its vertices. By rescaling, it can be given unit side length. This results in the simplex whose vertices are:

: <math>\frac{1}{\sqrt{2}}\mathbf{e}_i - \frac{1}{n\sqrt{2}}\bigg(1 \pm \frac{1}{\sqrt{n + 1}}\bigg) \cdot (1, \dots, 1),</math>

for <math>1 \le i \le n</math>, and

: <math>\pm\frac{1}{\sqrt{2(n + 1)}} \cdot (1, \dots, 1).</math>

Note that there are two sets of vertices described here. One set uses <math>+</math> in each calculation. The other set uses <math>-</math> in each calculation.

This simplex is inscribed in a hypersphere of radius <math>\sqrt{n/(2(n + 1))}</math>.

A different rescaling produces a simplex that is inscribed in a unit hypersphere. When this is done, its vertices are

: <math>\sqrt{1 + n^{-1}}\cdot\mathbf{e}_i - n^{-3/2}(\sqrt{n + 1} \pm 1) \cdot (1, \dots, 1),</math>

where <math>1 \le i \le n</math>, and

: <math>\pm n^{-1/2} \cdot (1, \dots, 1).</math>

The side length of this simplex is <math display="inline">\sqrt{2(n + 1)/n}</math>.

A highly symmetric way to construct a regular {{mvar|n}}-simplex is to use a representation of the [[cyclic group]] {{math|'''Z'''<sub>''n''+1</sub>}} by [[Orthogonal matrix|orthogonal matrices]]. This is an {{math|''n'' × ''n''}} orthogonal matrix {{mvar|Q}} such that {{math|1=''Q''<sup>''n''+1</sup> = ''I''}} is the [[identity matrix]], but no lower power of {{mvar|Q}} is. Applying powers of this [[Matrix (mathematics)|matrix]] to an appropriate vector {{math|'''v'''}} will produce the vertices of a regular {{mvar|n}}-simplex. To carry this out, first observe that for any orthogonal matrix {{mvar|Q}}, there is a choice of basis in which {{mvar|Q}} is a block diagonal matrix

: <math>Q = \operatorname{diag}(Q_1, Q_2, \dots, Q_k),</math>

where each {{math|''Q''<sub>''i''</sub>}} is orthogonal and either {{math|2 × 2}} or {{math|1 × 1}}. In order for {{mvar|Q}} to have order {{math|''n'' + 1}}, all of these matrices must have order [[Divisor|dividing]] {{math|''n'' + 1}}. Therefore each {{math|''Q''<sub>''i''</sub>}} is either a {{math|1 × 1}} matrix whose only entry is {{math|1}} or, if {{mvar|n}} is [[Parity (mathematics)|odd]], {{math|−1}}; or it is a {{math|2 × 2}} matrix of the form

: <math>\begin{pmatrix}
\cos \frac{2\pi\omega_i}{n + 1} & -\sin \frac{2\pi\omega_i}{n + 1} \\
\sin \frac{2\pi\omega_i}{n + 1} & \cos \frac{2\pi\omega_i}{n + 1}
\end{pmatrix},</math>

where each {{math|''ω''<sub>''i''</sub>}} is an [[integer]] between zero and {{mvar|n}} inclusive. A sufficient condition for the orbit of a point to be a regular simplex is that the matrices {{math|''Q''<sub>''i''</sub>}} form a basis for the non-trivial irreducible real representations of {{math|'''Z'''<sub>''n''+1</sub>}}, and the vector being rotated is not stabilized by any of them.

In practical terms, for {{mvar|n}} [[Parity (mathematics)|even]] this means that every matrix {{math|''Q''<sub>''i''</sub>}} is {{math|2 × 2}}, there is an equality of sets

: <math>\{\omega_1, n + 1 - \omega_1, \dots, \omega_{n/2}, n + 1 - \omega_{n/2}\} = \{1, \dots, n\},</math>

and, for every {{math|''Q''<sub>''i''</sub>}}, the entries of {{math|'''v'''}} upon which {{math|''Q''<sub>''i''</sub>}} acts are not both zero. For example, when {{math|1=''n'' = 4}}, one possible matrix is

: <math>\begin{pmatrix}
\cos(2\pi/5) & -\sin(2\pi/5) & 0 & 0 \\
\sin(2\pi/5) & \cos(2\pi/5) & 0 & 0 \\
0 & 0 & \cos(4\pi/5) & -\sin(4\pi/5) \\
0 & 0 & \sin(4\pi/5) & \cos(4\pi/5)
\end{pmatrix}.</math>

Applying this to the vector {{math|(1, 0, 1, 0)}} results in the simplex whose vertices are

: <math>
\begin{pmatrix} 1 \\ 0 \\ 1 \\ 0 \end{pmatrix},
\begin{pmatrix} \cos(2\pi/5) \\ \sin(2\pi/5) \\ \cos(4\pi/5) \\ \sin(4\pi/5) \end{pmatrix},
\begin{pmatrix} \cos(4\pi/5) \\ \sin(4\pi/5) \\ \cos(8\pi/5) \\ \sin(8\pi/5) \end{pmatrix},
\begin{pmatrix} \cos(6\pi/5) \\ \sin(6\pi/5) \\ \cos(2\pi/5) \\ \sin(2\pi/5) \end{pmatrix},
\begin{pmatrix} \cos(8\pi/5) \\ \sin(8\pi/5) \\ \cos(6\pi/5) \\ \sin(6\pi/5) \end{pmatrix},
</math>

each of which has distance √5 from the others. When {{mvar|n}} is odd, the condition means that exactly one of the diagonal blocks is {{math|1 × 1}}, equal to {{math|−1}}, and acts upon a non-zero entry of {{math|'''v'''}}; while the remaining diagonal blocks, say {{math|''Q''<sub>1</sub>, ..., ''Q''<sub>(''n'' − 1) / 2</sub>}}, are {{math|2 × 2}}, there is an equality of sets

: <math>\left\{\omega_1, -\omega_1, \dots, \omega_{(n-1)/2}, -\omega_{n-1)/2}\right\} = \left\{1, \dots, (n-1)/2, (n+3)/2, \dots, n \right\},</math>

and each diagonal block acts upon a pair of entries of {{math|'''v'''}} which are not both zero. So, for example, when {{math|1=''n'' = 3}}, the matrix can be

: <math>\begin{pmatrix}
0 & -1 & 0 \\
1 & 0 & 0 \\
0 & 0 & -1 \\
\end{pmatrix}.</math>

For the vector {{math|(1, 0, 1/{{radic|2}})}}, the resulting simplex has vertices

: <math>
\begin{pmatrix} 1 \\ 0 \\ 1/\surd2 \end{pmatrix},
\begin{pmatrix} 0 \\ 1 \\ -1/\surd2 \end{pmatrix},
\begin{pmatrix} -1 \\ 0 \\ 1/\surd2 \end{pmatrix},
\begin{pmatrix} 0 \\ -1 \\ -1/\surd2 \end{pmatrix},
</math>

each of which has distance 2 from the others.

== Geometric properties ==

=== Volume ===
The [[volume]] of an {{mvar|n}}-simplex in {{mvar|n}}-dimensional space with vertices {{math|(''v''<sub>0</sub>, ..., ''v''<sub>''n''</sub>)}} is

: <math>
\mathrm{Volume} = \frac{1}{n!} \left|\det
\begin{pmatrix}
v_1-v_0 && v_2-v_0 && \cdots && v_n-v_0
\end{pmatrix}\right|
</math>

where each column of the {{math|''n'' × ''n''}} [[determinant]] is a [[Vector (geometry)|vector]] that points from vertex {{math|''v''{{sub|0}}}} to another vertex {{math|''v''{{sub|''k''}}}}.<ref>A derivation of a very similar formula can be found in {{cite journal|last1=Stein|first1=P.|year=1966|title=A Note on the Volume of a Simplex|journal=American Mathematical Monthly|volume=73|issue=3|pages=299–301|doi=10.2307/2315353|jstor=2315353}}</ref> This formula is particularly useful when <math>v_0</math> is the origin.

The expression

: <math>
\mathrm{Volume} = \frac{1}{n!} \det\left[
\begin{pmatrix}
v_1^\text{T}-v_0^\text{T} \\ v_2^\text{T}-v_0^\text{T} \\ \vdots \\ v_n^\text{T}-v_0^\text{T}
\end{pmatrix}
\begin{pmatrix}
v_1-v_0 & v_2-v_0 & \cdots & v_n-v_0
\end{pmatrix}
\right]^{1/2}
</math>

employs a [[Gram determinant]] and works even when the {{mvar|n}}-simplex's vertices are in a Euclidean space with more than {{mvar|n}} dimensions, e.g., a triangle in <math>\mathbf{R}^3</math>.

A more symmetric way to compute the volume of an {{mvar|n}}-simplex in <math>\mathbf{R}^n</math> is

: <math>
\mathrm{Volume} = {1\over n!} \left|\det
\begin{pmatrix}
v_0 & v_1 & \cdots & v_n \\
1 & 1 & \cdots & 1
\end{pmatrix}\right|.
</math>

Another common way of computing the volume of the simplex is via the [[Cayley–Menger determinant]], which works even when the n-simplex's vertices are in a Euclidean space with more than n dimensions.<ref>{{mathworld|title=Cayley-Menger Determinant|author=Colins, Karen D.}}</ref>

Without the {{math|1/''n''!}} it is the formula for the volume of an {{mvar|n}}-[[Parallelepiped#Parallelotope|parallelotope]]. This can be understood as follows: Assume that {{mvar|P}} is an {{mvar|n}}-parallelotope constructed on a basis <math>(v_0, e_1, \ldots, e_n)</math> of <math>\mathbf{R}^n</math>. Given a [[permutation]] <math>\sigma</math> of <math>\{1,2,\ldots, n\}</math>, call a list of vertices <math>v_0,\ v_1, \ldots, v_n</math> a {{mvar|n}}-path if

: <math>v_1 = v_0 + e_{\sigma(1)},\ v_2 = v_1 + e_{\sigma(2)},\ldots, v_n = v_{n-1}+e_{\sigma(n)}</math>

(so there are {{math|''n''!}} {{mvar|n}}-paths and <math>v_n</math> does not depend on the permutation). The following assertions hold:

If {{mvar|P}} is the unit {{mvar|n}}-hypercube, then the union of the {{mvar|n}}-simplexes formed by the convex hull of each {{mvar|n}}-path is {{mvar|P}}, and these simplexes are congruent and pairwise non-overlapping.<ref>Every {{mvar|n}}-path corresponding to a permutation <math>\scriptstyle \sigma</math> is the image of the {{mvar|n}}-path <math>\scriptstyle v_0,\ v_0+e_1,\ v_0+e_1+e_2,\ldots v_0+e_1+\cdots + e_n</math> by the affine isometry that sends <math>\scriptstyle v_0</math> to <math>\scriptstyle v_0</math>, and whose linear part matches <math>\scriptstyle e_i</math> to <math>\scriptstyle e_{\sigma(i)}</math> for all&nbsp;{{mvar|i}}. hence every two {{mvar|n}}-paths are isometric, and so is their convex hulls; this explains the congruence of the simplexes. To show the other assertions, it suffices to remark that the interior of the simplex determined by the {{mvar|n}}-path <math>\scriptstyle v_0,\ v_0+e_{\sigma(1)},\ v_0+e_{\sigma(1)}+e_{\sigma(2)}\ldots v_0+e_{\sigma(1)}+\cdots + e_{\sigma(n)}</math> is the set of points <math>\scriptstyle v_0 + (x_1+\cdots +x_n) e_{\sigma(1)} + \cdots + (x_{n-1}+x_n) e_{\sigma(n-1)} + x_n e_{\sigma(n)}</math>, with <math>\scriptstyle 0< x_i < 1</math> and <math>\scriptstyle x_1+\cdots + x_n < 1.</math> Hence the components of these points with respect to each corresponding permuted basis are strictly ordered in the decreasing order. That explains why the simplexes are non-overlapping. The fact that the union of the simplexes is the whole unit {{mvar|n}}-hypercube follows as well, replacing the strict inequalities above by "<math>\scriptstyle \leq</math>". The same arguments are also valid for a general parallelotope, except the isometry between the simplexes.</ref> In particular, the volume of such a simplex is

: <math> \frac{\operatorname{Vol}(P)}{n!} = \frac 1 {n!}.</math>

If {{mvar|P}} is a general parallelotope, the same assertions hold except that it is no longer true, in dimension&nbsp;> 2, that the simplexes need to be pairwise congruent; yet their volumes remain equal, because the {{mvar|n}}-parallelotope is the image of the unit {{mvar|n}}-hypercube by the [[linear isomorphism]] that sends the canonical basis of <math>\mathbf{R}^n</math> to <math>e_1,\ldots, e_n</math>. As previously, this implies that the volume of a simplex coming from a {{mvar|n}}-path is:

: <math> \frac{\operatorname{Vol}(P)}{n!} = \frac{\det(e_1, \ldots, e_n)}{n!}.</math>

Conversely, given an {{mvar|n}}-simplex <math>(v_0,\ v_1,\ v_2,\ldots v_n)</math> of <math>\mathbf R^n</math>, it can be supposed that the vectors <math>e_1 = v_1-v_0,\ e_2 = v_2-v_1,\ldots e_n=v_n-v_{n-1}</math> form a basis of <math>\mathbf R^n</math>. Considering the parallelotope constructed from <math>v_0</math> and <math>e_1,\ldots, e_n</math>, one sees that the previous formula is valid for every simplex.

Finally, the formula at the beginning of this section is obtained by observing that

: <math>\det(v_1-v_0, v_2-v_0,\ldots, v_n-v_0) = \det(v_1-v_0, v_2-v_1,\ldots, v_n-v_{n-1}).</math>

From this formula, it follows immediately that the volume under a standard {{mvar|n}}-simplex (i.e. between the origin and the simplex in {{math|'''R'''<sup>''n''+1</sup>}}) is

: <math>{1 \over (n+1)!}</math>

The volume of a regular {{mvar|n}}-simplex with unit side length is

: <math>\frac{\sqrt{n+1}}{n!\sqrt{2^n}}</math>

as can be seen by multiplying the previous formula by {{math|''x''<sup>''n''+1</sup>}}, to get the volume under the {{mvar|n}}-simplex as a function of its vertex distance {{mvar|x}} from the origin, differentiating with respect to {{mvar|x}}, at <math>x=1/\sqrt{2}</math>&nbsp; (where the {{mvar|n}}-simplex side length is 1), and normalizing by the length <math>dx/\sqrt{n+1}</math> of the increment, <math>(dx/(n+1),\ldots, dx/(n+1))</math>, along the normal vector.

=== Dihedral angles of the regular ''n''-simplex ===
Any two {{math|(''n'' − 1)}}-dimensional faces of a regular {{mvar|n}}-dimensional simplex are themselves regular {{math|(''n'' − 1)}}-dimensional simplices, and they have the same [[dihedral angle]] of {{math|cos<sup>−1</sup>(1/''n'')}}.<ref>{{cite journal|last1=Parks|first1=Harold R.|author-link=Harold R. Parks|last2=Wills|first2=Dean C.|date=October 2002|title=An Elementary Calculation of the Dihedral Angle of the Regular {{mvar|n}}-Simplex|journal=American Mathematical Monthly|volume=109|issue=8|pages=756–8|doi=10.2307/3072403|jstor=3072403}}</ref><ref>{{cite thesis|type=PhD|publisher=Oregon State University|date=June 2009|title=Connections between combinatorics of permutations and algorithms and geometry|first1=Harold R.|last2=Parks|first2=Dean C.|last1=Wills|url=http://ir.library.oregonstate.edu/xmlui/handle/1957/11929|hdl=1957/11929}}</ref>

This can be seen by noting that the center of the standard simplex is <math display="inline">\left(\frac{1}{n+1}, \dots, \frac{1}{n+1}\right)</math>, and the centers of its faces are coordinate permutations of <math display="inline">\left(0, \frac{1}{n}, \dots, \frac{1}{n}\right)</math>. Then, by symmetry, the vector pointing from <math display="inline">\left(\frac{1}{n+1}, \dots, \frac{1}{n+1}\right)</math> to <math display="inline">\left(0, \frac{1}{n}, \dots, \frac{1}{n}\right)</math> is perpendicular to the faces. So the vectors normal to the faces are permutations of <math>(-n, 1, \dots, 1)</math>, from which the dihedral angles are calculated.

=== Simplices with an "orthogonal corner" ===
An "orthogonal corner" means here that there is a vertex at which all adjacent edges are pairwise orthogonal. It immediately follows that all adjacent [[Face (geometry)|faces]] are pairwise orthogonal. Such simplices are generalizations of right triangles and for them there exists an {{mvar|n}}-dimensional version of the [[Pythagorean theorem]]:

The sum of the squared {{math|(''n'' − 1)}}-dimensional volumes of the facets adjacent to the orthogonal corner equals the squared {{math|(''n'' − 1)}}-dimensional volume of the facet opposite of the orthogonal corner.

: <math> \sum_{k=1}^n |A_k|^2 = |A_0|^2 </math>

where <math> A_1 \ldots A_n </math> are facets being pairwise orthogonal to each other but not orthogonal to <math>A_0</math>, which is the facet opposite the orthogonal corner.<ref>{{cite journal|last1=Donchian|first1=P. S.|last2=Coxeter|first2=H. S. M.|date=July 1935|title=1142. An n-dimensional extension of Pythagoras' Theorem|journal=The Mathematical Gazette|volume=19|issue=234|pages=206|doi=10.2307/3605876|jstor=3605876|s2cid=125391795}}</ref>

For a 2-simplex, the theorem is the [[Pythagorean theorem]] for triangles with a right angle and for a 3-simplex it is [[de Gua's theorem]] for a tetrahedron with an orthogonal corner.

=== Relation to the (''n'' + 1)-hypercube ===
The [[Hasse diagram]] of the face lattice of an {{mvar|n}}-simplex is isomorphic to the graph of the {{math|(''n'' + 1)}}-[[hypercube]]'s edges, with the hypercube's vertices mapping to each of the {{mvar|n}}-simplex's elements, including the entire simplex and the null polytope as the extreme points of the lattice (mapped to two opposite vertices on the hypercube). This fact may be used to efficiently enumerate the simplex's face lattice, since more general face lattice enumeration algorithms are more computationally expensive.

The {{mvar|n}}-simplex is also the [[vertex figure]] of the {{math|(''n'' + 1)}}-hypercube. It is also the [[Facet (geometry)|facet]] of the {{math|(''n'' + 1)}}-[[orthoplex]].

=== Topology ===
[[Topology|Topologically]], an {{mvar|n}}-simplex is [[Topologically equivalent|equivalent]] to an [[Ball (mathematics)|{{mvar|n}}-ball]]. Every {{mvar|n}}-simplex is an {{mvar|n}}-dimensional [[manifold with corners]].

=== Probability ===
{{Main|Categorical distribution}}
In probability theory, the points of the standard {{mvar|n}}-simplex in {{math|(''n'' + 1)}}-space form the space of possible probability distributions on a finite set consisting of {{math|''n'' + 1}} possible outcomes. The correspondence is as follows: For each distribution described as an ordered {{math|(''n'' + 1)}}-tuple of probabilities whose sum is (necessarily) 1, we associate the point of the simplex whose [[barycentric coordinates]] are precisely those probabilities. That is, the {{mvar|k}}th vertex of the simplex is assigned to have the {{mvar|k}}th probability of the {{math|(''n'' + 1)}}-tuple as its barycentric coefficient. This correspondence is an affine homeomorphism.

=== Compounds ===
Since all simplices are self-dual, they can form a series of compounds;

* Two triangles form a [[hexagram]] {6/2}.
* Two tetrahedra form a [[compound of two tetrahedra]] or [[Stellated octahedron|stella octangula]].
* Two 5-cells form a [[compound of two 5-cells]] in four dimensions.

== Algebraic topology ==<!--'Singular n-simplex' redirects here-->
In [[algebraic topology]], simplices are used as building blocks to construct an interesting class of [[Topological space|topological spaces]] called [[Simplicial complex|simplicial complexes]]. These spaces are built from simplices glued together in a [[Combinatorics|combinatorial]] fashion. Simplicial complexes are used to define a certain kind of [[Homology (mathematics)|homology]] called [[simplicial homology]].

A finite set of {{mvar|k}}-simplexes embedded in an [[open subset]] of {{math|'''R'''<sup>''n''</sup>}} is called an '''affine {{mvar|k}}-chain'''. The simplexes in a chain need not be unique; they may occur with [[Multiplicity (mathematics)|multiplicity]]. Rather than using standard set notation to denote an affine chain, it is instead the standard practice to use plus signs to separate each member in the set. If some of the simplexes have the opposite [[Orientability|orientation]], these are prefixed by a minus sign. If some of the simplexes occur in the set more than once, these are prefixed with an integer count. Thus, an affine chain takes the symbolic form of a sum with integer coefficients.

Note that each facet of an {{mvar|n}}-simplex is an affine {{math|(''n'' − 1)}}-simplex, and thus the [[Boundary (topology)|boundary]] of an {{mvar|n}}-simplex is an affine {{math|(''n'' − 1)}}-chain. Thus, if we denote one positively oriented affine simplex as

: <math>\sigma=[v_0,v_1,v_2,\ldots,v_n]</math>

with the <math>v_j</math> denoting the vertices, then the boundary <math>\partial\sigma</math> of {{mvar|σ}} is the chain

: <math>\partial\sigma = \sum_{j=0}^n (-1)^j [v_0,\ldots,v_{j-1},v_{j+1},\ldots,v_n].</math>

It follows from this expression, and the linearity of the boundary operator, that the boundary of the boundary of a simplex is zero:

: <math>\partial^2\sigma = \partial \left( \sum_{j=0}^n (-1)^j [v_0,\ldots,v_{j-1},v_{j+1},\ldots,v_n] \right) = 0. </math>

Likewise, the boundary of the boundary of a chain is zero: <math> \partial ^2 \rho =0 </math>.

More generally, a simplex (and a chain) can be embedded into a [[manifold]] by means of smooth, differentiable map <math>f:\mathbf{R}^n \to M</math>. In this case, both the summation convention for denoting the set, and the boundary operation commute with the [[embedding]]. That is,

: <math>f \left(\sum\nolimits_i a_i \sigma_i \right) = \sum\nolimits_i a_i f(\sigma_i)</math>

where the <math>a_i</math> are the integers denoting orientation and multiplicity. For the boundary operator <math>\partial</math>, one has:

: <math>\partial f(\rho) = f (\partial \rho)</math>

where {{mvar|ρ}} is a chain. The boundary operation commutes with the mapping because, in the end, the chain is defined as a set and little more, and the set operation always commutes with the [[Function (mathematics)|map operation]] (by definition of a map).

A [[Continuous function (topology)|continuous map]] <math>f: \sigma \to X</math> to a [[topological space]] {{mvar|X}} is frequently referred to as a '''singular {{mvar|n}}-simplex'''<!--boldface per WP:R#PLA-->. (A map is generally called "singular" if it fails to have some desirable property such as continuity and, in this case, the term is meant to reflect to the fact that the continuous map need not be an embedding.)<ref>{{cite book|last=Lee|first=John M.|date=2006|url=https://books.google.com/books?id=AdIRBwAAQBAJ&pg=PR1|title=Introduction to Topological Manifolds|publisher=Springer|isbn=978-0-387-22727-6|pages=292–3}}</ref>

== Algebraic geometry ==
Since classical [[algebraic geometry]] allows one to talk about polynomial equations but not inequalities, the ''algebraic standard n-simplex'' is commonly defined as the subset of affine {{math|(''n'' + 1)}}-dimensional space, where all coordinates sum up to 1 (thus leaving out the inequality part). The algebraic description of this set is<math display="block">\Delta^n := \left\{x \in \mathbb{A}^{n+1} ~\Bigg|~ \sum_{i=1}^{n+1} x_i = 1\right\},</math>which equals the [[Scheme (mathematics)|scheme]]-theoretic description <math>\Delta_n(R) = \operatorname{Spec}(R[\Delta^n])</math> with<math display="block">R[\Delta^n] := R[x_1,\ldots,x_{n+1}]\left/\left(1-\sum x_i \right)\right.</math>the ring of regular functions on the algebraic {{mvar|n}}-simplex (for any [[Ring (mathematics)|ring]] <math>R</math>).

By using the same definitions as for the classical {{mvar|n}}-simplex, the {{mvar|n}}-simplices for different dimensions {{mvar|n}} assemble into one [[simplicial object]], while the rings <math>R[\Delta^n]</math> assemble into one cosimplicial object <math>R[\Delta^\bullet]</math> (in the [[Category (mathematics)|category]] of schemes resp. rings, since the face and degeneracy maps are all polynomial).

The algebraic {{mvar|n}}-simplices are used in higher [[K-theory|''K''-theory]] and in the definition of higher [[Chow group|Chow groups]].

== Applications ==

* In [[statistics]], simplices are sample spaces of [[compositional data]] and are also used in plotting quantities that sum to 1, such as proportions of subpopulations, as in a [[ternary plot]].
* In [[probability theory]], a simplex space is often used to represent the space of probability distributions. The [[Dirichlet distribution]], for instance, is defined on a simplex.
* In [[Applied statistics#industrial|industrial statistics]], simplices arise in problem formulation and in algorithmic solution. In the design of bread, the producer must combine yeast, flour, water, sugar, etc. In such [[Mixture|mixtures]], only the relative proportions of ingredients matters: For an optimal bread mixture, if the flour is doubled then the yeast should be doubled. Such mixture problem are often formulated with normalized constraints, so that the nonnegative components sum to one, in which case the feasible region forms a simplex. The quality of the bread mixtures can be estimated using [[response surface methodology]], and then a local maximum can be computed using a [[nonlinear programming]] method, such as [[sequential quadratic programming]].<ref>{{cite book|author=Cornell, John|year=2002|title=Experiments with Mixtures: Designs, Models, and the Analysis of Mixture Data|publisher=Wiley|isbn=0-471-07916-2|edition=third}}</ref>
* In [[operations research]], [[linear programming]] problems can be solved by the [[simplex algorithm]] of [[George Dantzig]].
* In [[game theory]], strategies can be represented as points within a simplex. This representation simplifies the analysis of mixed strategies.
* In [[geometric design]] and [[computer graphics]], many methods first perform simplicial [[Triangulation (topology)|triangulations]] of the domain and then [[Interpolation|fit interpolating]] [[Polynomial and rational function modeling|polynomials]] to each simplex.<ref>{{cite journal|last=Vondran|first=Gary L.|date=April 1998|title=Radial and Pruned Tetrahedral Interpolation Techniques|url=http://www.hpl.hp.com/techreports/98/HPL-98-95.pdf|journal=HP Technical Report|volume=HPL-98-95|pages=1–32|archive-url=https://web.archive.org/web/20110607102757/http://www.hpl.hp.com/techreports/98/HPL-98-95.pdf|archive-date=2011-06-07|access-date=2009-11-11|url-status=dead}}</ref>
* In [[chemistry]], the hydrides of most elements in the [[p-block]] can resemble a simplex if one is to connect each atom. [[Neon]] does not react with hydrogen and as such is [[Monatomic gas|a point]], [[fluorine]] bonds with one hydrogen atom and forms a line segment, [[oxygen]] bonds with two hydrogen atoms in a [[Bent molecular geometry|bent]] fashion resembling a triangle, [[nitrogen]] reacts to form a [[Trigonal pyramidal molecular geometry|tetrahedron]], and [[carbon]] forms [[Tetrahedral molecular geometry|a structure]] resembling a [[Schlegel diagram]] of the 5-cell. This trend continues for the heavier analogues of each element, as well as if the hydrogen atom is replaced by a [[halogen]] atom.
* In some approaches to [[quantum gravity]], such as [[Regge calculus]] and [[Causal dynamical triangulation|causal dynamical triangulations]], simplices are used as building blocks of discretizations of spacetime; that is, to build [[Simplicial manifold|simplicial manifolds]].

== See also ==
{{colbegin}}
* [[3-sphere]]
* [[Aitchison geometry]]
* [[Causal dynamical triangulation]]
* [[Complete graph]]
* [[Delaunay triangulation]]
* [[Distance geometry]]
* [[Geometric primitive]]
* [[Hill tetrahedron]]
* [[Hypersimplex]]
* [[List of regular polytopes]]
* [[Metcalfe's law]]
* Other regular {{mvar|n}}-[[polytope]]s
** [[Cross-polytope]]
** [[Hypercube]]
** [[Tesseract]]
* [[Polytope]]
* [[Schläfli orthoscheme]]
* [[Simplex algorithm]] – an optimization method with inequality constraints
* [[Simplicial complex]]
* [[Simplicial homology]]
* [[Simplicial set]]
* [[Spectrahedron]]
* [[Ternary plot]]
{{colend}}

== Notes ==
{{reflist|30em}}

== References ==

* {{cite book|last=Rudin|first=Walter|year=1976|title=Principles of Mathematical Analysis|publisher=McGraw-Hill|isbn=0-07-054235-X|edition=3rd|author-link=Walter Rudin}} ''(See chapter 10 for a simple review of topological properties.)''
* {{cite book|last=Tanenbaum|first=Andrew S.|year=2003|title=Computer Networks|publisher=Prentice Hall|isbn=0-13-066102-3|edition=4th|chapter=§2.5.3|author-link=Andrew S. Tanenbaum}}
* {{cite book|last=Devroye|first=Luc|year=1986|url=http://cg.scs.carleton.ca/~luc/rnbookindex.html|title=Non-Uniform Random Variate Generation|publisher=Springer|isbn=0-387-96305-7|archive-url=https://web.archive.org/web/20090505034911/http://cg.scs.carleton.ca/~luc/rnbookindex.html|archive-date=2009-05-05}}
* {{Cite book|last=Coxeter|first=H.S.M.|year=1973|title=Regular Polytopes|title-link=Regular Polytopes (book)|publisher=Dover|isbn=0-486-61480-8|edition=3rd|author-link=Harold Scott MacDonald Coxeter}}
** pp.&nbsp;120–121, §7.2. see illustration 7-2<small>A</small>
** p.&nbsp;296, Table I (iii): Regular Polytopes, three regular polytopes in {{mvar|n}} dimensions ({{math|''n'' ≥ 5}})
* {{mathworld|urlname=Simplex|title=Simplex}}
* {{cite book|last1=Boyd|first1=Stephen|last2=Vandenberghe|first2=Lieven|date=2004|url=https://books.google.com/books?id=IUZdAAAAQBAJ|title=Convex Optimization|publisher=Cambridge University Press|isbn=978-1-107-39400-1|author-link=Stephen P. Boyd|author2-link=Lieven Vandenberghe}} As [https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf PDF]

{{Dimension topics}} {{Polytopes}}

Revisi per 7 Maret 2024 04.15

Keserupaan matriks


Untuk kegunaan lain, lihat Similarity (geometry) dan Similarity transformation (disambiguation).
Artikel ini bukan mengenai Matriks keserupaan.

In linear algebra, two n-by-n matrices A and B are called similar if there exists an invertible n-by-n matrix P such that

Similar matrices represent the same linear map under two (possibly) different bases, with P being the change of basis matrix.[1][2]

A transformation A ↦ P−1AP is called a similarity transformation or conjugation of the matrix A. In the general linear group, similarity is therefore the same as conjugacy, and similar matrices are also called conjugate; however, in a given subgroup H of the general linear group, the notion of conjugacy may be more restrictive than similarity, since it requires that P be chosen to lie in H.

Motivating example

When defining a linear transformation, it can be the case that a change of basis can result in a simpler form of the same transformation. For example, the matrix representing a rotation in R3 when the axis of rotation is not aligned with the coordinate axis can be complicated to compute. If the axis of rotation were aligned with the positive z-axis, then it would simply be

where is the angle of rotation. In the new coordinate system, the transformation would be written as
where x' and y' are respectively the original and transformed vectors in a new basis containing a vector parallel to the axis of rotation. In the original basis, the transform would be written as
where vectors x and y and the unknown transform matrix T are in the original basis. To write T in terms of the simpler matrix, we use the change-of-basis matrix P that transforms x and y as and :
Thus, the matrix in the original basis, , is given by . The transform in the original basis is found to be the product of three easy-to-derive matrices. In effect, the similarity transform operates in three steps: change to a new basis (P), perform the simple transformation (S), and change back to the old basis (P−1).

Properties

Similarity is an equivalence relation on the space of square matrices.

Because matrices are similar if and only if they represent the same linear operator with respect to (possibly) different bases, similar matrices share all properties of their shared underlying operator:

Because of this, for a given matrix A, one is interested in finding a simple "normal form" B which is similar to A—the study of A then reduces to the study of the simpler matrix B.

Similarity of matrices does not depend on the base field: if L is a field containing K as a subfield, and A and B are two matrices over K, then A and B are similar as matrices over K if and only if they are similar as matrices over L. This is so because the rational canonical form over K is also the rational canonical form over L. This means that one may use Jordan forms that only exist over a larger field to determine whether the given matrices are similar.

In the definition of similarity, if the matrix P can be chosen to be a matriks permutasi then A and B are permutation-similar; if P can be chosen to be a unitary matrix then A and B are unitarily equivalent. The spectral theorem says that every normal matrix is unitarily equivalent to some diagonal matrix. Specht's theorem states that two matrices are unitarily equivalent if and only if they satisfy certain trace equalities.

Lihat pula

Referensi

Kutipan

  1. ^ Beauregard, Raymond A.; Fraleigh, John B. (1973). A First Course In Linear Algebra: with Optional Introduction to Groups, Rings, and FieldsPerlu mendaftar (gratis). Boston: Houghton Mifflin Co. hlm. 240–243. ISBN 0-395-14017-X. 
  2. ^ Bronson, Richard (1970), Matrix Methods: An Introduction, New York: Academic Press, hlm. 176–178, LCCN 70097490 

Pustaka

  • Horn, Roger A.; Johnson, Charles R. (1985). Matrix Analysis. Cambridge University Press. ISBN 0-521-38632-2.  (Similarity is discussed many places, starting at page 44.)
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