Content deleted Content added
No edit summary |
|||
| (120 intermediate revisions by 82 users not shown) | |||
Line 1:
{{Short description|Special kind of square matrix}}
{{
{{for|the rings|triangular matrix ring}}{{Redirects here|Triangularization|the geometric process|Triangulation}}
In mathematics, a '''triangular matrix''' is a special kind of [[square matrix]]. A square matrix is called '''{{visible anchor|lower triangular}}''' if all the entries ''above'' the [[main diagonal]] are zero. Similarly, a square matrix is called '''{{visible anchor|upper triangular}}''' if all the entries ''below'' the main diagonal are zero.
Because matrix equations with triangular matrices are easier to solve, they are very important in [[numerical analysis]]. By the [[LU decomposition]] algorithm, an [[invertible matrix]] may be written as the [[matrix multiplication|product]] of a lower triangular matrix ''L'' and an upper triangular matrix ''U'' [[if and only if]] all its leading principal [[minor (linear algebra)|minor]]s are non-zero.
== Description ==
A matrix of the form
:<math>
\ell_{1,1} & & & & 0 \\
\
\end{bmatrix}</math>
is called a '''lower triangular matrix''' or '''left triangular matrix''', and analogously a matrix of the form
:<math>
u_{1,1} & u_{1,2} & u_{1,3} & \ldots & u_{1,n} \\
&
& &
0 & & &
\end{bmatrix}</math>
is called an '''upper triangular matrix''' or '''right triangular matrix'''.
A matrix that is both upper and lower triangular is [[diagonal matrix|diagonal]]. Matrices that are [[similar (linear algebra)|similar]] to triangular matrices are called '''triangularisable'''.
A non-square (or sometimes any) matrix with zeros above (below) the diagonal is called a lower (upper) trapezoidal matrix. The non-zero entries form the shape of a [[trapezoid]].
===Examples===
The matrix
:<math>\begin{bmatrix}
1 & 0 & 0 \\
2 & 96 & 0 \\
4 & 9 & 69
\end{bmatrix}</math>
is the lower triangular for the non symmetric matrix:
:<math>\begin{bmatrix}
1 &
\end{bmatrix}</math>
and
:<math>\begin{bmatrix}
1 &
\end{bmatrix}</math>
is
:<math>\begin{bmatrix}
1 & 4 & 1 \\
99 & 6 & 9 \\
40 & 88 & 1
\end{bmatrix}</math>
==Forward and back substitution==
<!-- Section is linked from several redirects (Back substitution etc.) – please update if you change the section title -->
A matrix equation in the form <math>L\mathbf{x} = \mathbf{b}</math> or <math>U\mathbf{x} = \mathbf{b}</math> is very easy to solve by an iterative process called '''forward substitution''' for lower triangular matrices and analogously '''back substitution''' for upper triangular matrices. The process is so called because for lower triangular matrices, one first computes <math>x_1</math>, then substitutes that ''forward'' into the ''next'' equation to solve for <math>x_2</math>, and repeats through to <math>x_n</math>. In an upper triangular matrix, one works ''backwards,'' first computing <math>x_n</math>, then substituting that ''back'' into the ''previous'' equation to solve for <math>x_{n-1}</math>, and repeating through <math>x_1</math>.
Notice that this does not require inverting the matrix.
===Forward substitution===
The matrix equation ''L'''''x''' = '''b''' can be written as a system of linear equations
:<math>\begin{matrix}
\ell_{1,1} x_1 & & & & & & & = & b_1 \\
\ell_{2,1} x_1 & + & \ell_{2,2} x_2 & & & & & = & b_2 \\
\vdots & & \vdots & & \ddots & & & & \vdots \\
\ell_{m,1} x_1 & + & \ell_{m,2} x_2 & + & \dotsb & + & \ell_{m,m} x_m & = & b_m \\
\end{matrix}</math>
Observe that the first equation (<math>\ell_{1,1} x_1 = b_1</math>) only involves <math>x_1</math>, and thus one can solve for <math>x_1</math> directly. The second equation only involves <math>x_1</math> and <math>x_2</math>, and thus can be solved once one substitutes in the already solved value for <math>x_1</math>. Continuing in this way, the <math>k</math>-th equation only involves <math>x_1,\dots,x_k</math>, and one can solve for <math>x_k</math> using the previously solved values for <math>x_1,\dots,x_{k-1}</math>. The resulting formulas are:
:<math>\begin{align}
x_1 &= \frac{b_1}{\ell_{1,1}}, \\
x_2 &= \frac{b_2 - \ell_{2,1} x_1}{\ell_{2,2}}, \\
&\ \ \vdots \\
x_m &= \frac{b_m - \sum_{i=1}^{m-1} \ell_{m,i}x_i}{\ell_{m,m}}.
\end{align}</math>
A matrix equation with an upper triangular matrix ''U'' can be solved in an analogous way, only working backwards.
===Applications===
Forward substitution is used in financial [[Bootstrapping (finance)|bootstrapping]] to construct a [[yield curve]].
==Properties==
The [[transpose]] of an upper triangular matrix is a lower triangular matrix and vice versa.
A matrix which is both symmetric and triangular is diagonal.
In a similar vein, a matrix which is both [[normal matrix|normal]] (meaning ''A''<sup>*</sup>''A'' = ''AA''<sup>*</sup>, where ''A''<sup>*</sup> is the [[conjugate transpose]]) and triangular is also diagonal. This can be seen by looking at the diagonal entries of ''A''<sup>*</sup>''A'' and ''AA''<sup>*</sup>.
The [[determinant]] and [[Permanent (mathematics)|permanent]] of a triangular matrix equal the product of the diagonal entries, as can be checked by direct computation.
In fact more is true: the [[eigenvalue]]s of a triangular matrix are exactly its diagonal entries. Moreover, each eigenvalue occurs exactly ''k'' times on the diagonal, where ''k'' is its [[algebraic multiplicity]], that is, its [[Multiplicity of a root of a polynomial|multiplicity as a root]] of the [[characteristic polynomial]] <math>p_A(x)=\det(xI-A)</math> of ''A''. In other words, the characteristic polynomial of a triangular ''n''×''n'' matrix ''A'' is exactly
: <math>p_A(x) = (x-a_{11})(x-a_{22})\cdots(x-a_{nn})</math>,
that is, the unique degree ''n'' polynomial whose roots are the diagonal entries of ''A'' (with multiplicities).
To see this, observe that <math>xI-A</math> is also triangular and hence its determinant <math>\det(xI-A)</math> is the product of its diagonal entries <math>(x-a_{11})(x-a_{22})\cdots(x-a_{nn})</math>.<ref name="axler">{{Cite book |last = Axler | first = Sheldon Jay | title = Linear Algebra Done Right | date = 1997 | publisher = Springer | isbn = 0-387-22595-1 | edition = 2nd | ___location = New York | oclc = 54850562 | pages = 86–87, 169}}</ref>
==Special forms==
=== Unitriangular matrix ===
If the entries on the [[main diagonal]] of a (lower or upper) triangular matrix are all 1, the matrix is called (lower or upper) '''unitriangular'''.
Other names used for these matrices are '''unit''' (lower or upper) '''triangular''', or very rarely '''normed''' (lower or upper) '''triangular'''. However, a ''unit'' triangular matrix is not the same as '''the''' ''[[identity matrix|unit matrix]]'', and a ''normed'' triangular matrix has nothing to do with the notion of [[matrix norm]].
All finite unitriangular matrices are [[unipotent]].
=== Strictly triangular matrix ===
If all of the entries on the main diagonal of a (
All finite strictly triangular matrices are [[nilpotent matrix|nilpotent]] of index at most ''n'' as a consequence of the [[Cayley–Hamilton theorem|Cayley-Hamilton theorem]].
=== Atomic triangular matrix ===
{{Main|Frobenius matrix}}
An '''atomic''' (lower or upper) '''triangular matrix''' is a special form of unitriangular matrix, where all of the [[off-diagonal element]]s are zero, except for the entries in a single column. Such a matrix is also called a '''Frobenius matrix''', a '''Gauss matrix''', or a '''Gauss transformation matrix'''.
=== Block triangular matrix ===
{{Main|Block matrix}}
A block triangular matrix is a [[block matrix]] (partitioned matrix) that is a triangular matrix.
====Upper
A matrix <math>A</math> is '''upper block triangular''' if
:<math>A = \begin{bmatrix}
A_{11} & A_{12} & \cdots & A_{1k} \\
0 & A_{22} & \cdots & A_{2k} \\
0 &
\end{bmatrix}</math>,
where <math>A_{ij} \in \mathbb{F}^{n_i \times n_j}</math> for all <math>i, j = 1, \ldots, k</math>.<ref name="bernstein2009">{{Cite book |last=Bernstein |first=Dennis S. |title=Matrix mathematics: theory, facts, and formulas |publisher=Princeton University Press |year=2009 |isbn=978-0-691-14039-1 |edition=2 |___location=Princeton, NJ |pages=168 |language=en}}</ref>
====Lower block triangular====
A matrix <math>A</math> is '''lower block triangular''' if
:<math>A = \begin{bmatrix}
A_{11} & 0 & \cdots & 0 \\
A_{21} & A_{22} & \cdots & 0 \\
\vdots & \vdots & \ddots & \vdots \\
A_{k1} & A_{k2} & \cdots & A_{kk}
\end{bmatrix}</math>,
where <math>A_{ij} \in \mathbb{F}^{n_i \times n_j}</math> for all <math>i, j = 1, \ldots, k</math>.<ref name="bernstein2009" />
==Triangularisability{{Anchor|Triangularizability}}==
A matrix that is [[similar matrix|similar]] to a triangular matrix is referred to as '''
Any complex square matrix is
A more precise statement is given by the [[Jordan normal form]] theorem, which states that in this situation, ''A'' is similar to an upper triangular matrix of a very particular form. The simpler triangularization result is often sufficient however, and in any case used in proving the Jordan normal form theorem.<ref name="axler"/><ref name="herstein">{{
In the case of complex matrices, it is possible to say more about
===Simultaneous triangularisability===
Line 152 ⟶ 170:
The basic result is that (over an algebraically closed field), the [[commuting matrices]] <math>A,B</math> or more generally <math>A_1,\ldots,A_k</math> are simultaneously triangularizable. This can be proven by first showing that commuting matrices have a common eigenvector, and then inducting on dimension as before. This was proven by Frobenius, starting in 1878 for a commuting pair, as discussed at [[commuting matrices]]. As for a single matrix, over the complex numbers these can be triangularized by unitary matrices.
The fact that commuting matrices have a common eigenvector can be interpreted as a result of [[Hilbert's Nullstellensatz]]: commuting matrices form a commutative algebra <math>K[A_1,\ldots,A_k]</math> over <math>K[x_1,\ldots,x_k]</math> which can be interpreted as a variety in ''k''-dimensional affine space, and the existence of a (common) eigenvalue (and hence a common eigenvector) corresponds to this variety having a point (being non-empty), which is the content of the (weak) Nullstellensatz.{{Citation needed|reason=The existence of a common eigenvector is not clear, see https://mathoverflow.net/questions/43298/commuting-matrices-and-the-weak-nullstellensatz|date=March 2021}} In algebraic terms, these operators correspond to an [[algebra representation]] of the polynomial algebra in ''k'' variables.
This is generalized by [[Lie's theorem]], which shows that any representation of a [[solvable Lie algebra]] is simultaneously upper
More generally and precisely, a set of matrices <math>A_1,\ldots,A_k</math> is simultaneously triangularisable if and only if the matrix <math>p(A_1,\ldots,A_k)[A_i,A_j]</math> is [[nilpotent]] for all polynomials ''p'' in ''k'' ''non''-commuting variables, where <math>[A_i,A_j]</math> is the [[commutator]];
== Algebras of triangular matrices ==
[[File:Cyclic group Z4; Cayley table; powers of Gray code permutation (small).svg|thumb|[[Logical matrix|Binary]] lower unitriangular [[Toeplitz matrix|Toeplitz]] matrices, multiplied using [[Finite field|'''F'''<sub>2</sub>]] operations. They form the [[Cayley table]] of [[cyclic group|Z<sub>4</sub>]] and correspond to [[v:Gray code permutation powers#4 bit|powers of the 4-bit Gray code permutation]].]]
Upper triangularity is preserved by many operations:
* The sum of two upper triangular matrices is upper triangular.
* The product of two upper triangular matrices is upper triangular.
* The [[inverse matrix|inverse]] of an upper triangular matrix, if it exists, is upper triangular.
* The product of an upper triangular matrix and a scalar is upper triangular.
Together these facts mean that the upper triangular matrices form a [[subalgebra]] of the [[associative algebra]] of square matrices for a given size. Additionally, this also shows that the upper triangular matrices can be viewed as a Lie subalgebra of the [[Lie algebra]] of square matrices of a fixed size, where the [[Lie bracket]] [''a'', ''b''] given by the [[commutator#Ring theory|commutator]] {{nowrap|''ab − ba''}}. The Lie algebra of all upper triangular matrices is a [[solvable Lie algebra]]. It is often referred to as a [[Borel subalgebra]] of the Lie algebra of all square matrices.
All these results hold if ''upper triangular'' is replaced by ''lower triangular'' throughout; in particular the lower triangular matrices also form a Lie algebra. However, operations mixing upper and lower triangular matrices do not in general produce triangular matrices. For instance, the sum of an upper and a lower triangular matrix can be any matrix; the product of a lower triangular with an upper triangular matrix is not necessarily triangular either.
The set of unitriangular matrices forms a [[Lie group]].
The set of strictly upper (or lower) triangular matrices forms a [[nilpotent Lie algebra]], denoted <math>\mathfrak{n}.</math> This algebra is the [[derived Lie algebra]] of <math>\mathfrak{b}</math>, the Lie algebra of all upper triangular matrices; in symbols, <math>\mathfrak{n} = [\mathfrak{b},\mathfrak{b}].</math> In addition, <math>\mathfrak{n}</math> is the Lie algebra of the Lie group of unitriangular matrices.
In fact, by [[Engel's theorem]], any finite-dimensional nilpotent Lie algebra is conjugate to a subalgebra of the strictly upper triangular matrices, that is to say, a finite-dimensional nilpotent Lie algebra is simultaneously strictly upper triangularizable.
Algebras of upper triangular matrices have a natural generalization in [[functional analysis]] which yields [[nest algebra]]s on [[Hilbert space]]s.
{{see also|Affine group}}
===Borel subgroups and Borel subalgebras===
{{main|Borel subgroup|Borel subalgebra}}
The set of invertible triangular matrices of a given kind (lower or upper) forms a [[group (mathematics)|group]], indeed a [[Lie group]], which is a subgroup of the [[general linear group]] of all invertible matrices. A triangular matrix is invertible precisely when its diagonal entries are invertible (non-zero).
Over the real numbers, this group is disconnected, having <math>2^n</math> components accordingly as each diagonal entry is positive or negative. The identity component is invertible triangular matrices with positive entries on the diagonal, and the group of all invertible triangular matrices is a [[semidirect product]] of this group and the group of [[Diagonal matrix|diagonal matrices]] with <math>\pm 1</math> on the diagonal, corresponding to the components.
The [[Lie algebra]] of the Lie group of invertible upper triangular matrices is the set of all upper triangular matrices, not necessarily invertible, and is a [[solvable Lie algebra]]. These are, respectively, the standard [[Borel subgroup]] ''B'' of the Lie group GL<sub>''n''</sub> and the standard [[Borel subalgebra]] <math>\mathfrak{b}</math> of the Lie algebra gl<sub>''n''</sub>.
The upper triangular matrices are precisely those that stabilize the [[Flag (linear algebra)|standard flag]]. The invertible ones among them form a subgroup of the general linear group, whose conjugate subgroups are those defined as the stabilizer of some (other) complete flag. These subgroups are [[Borel subgroup]]s. The group of invertible lower triangular matrices is such a subgroup, since it is the stabilizer of the standard flag associated to the standard basis in reverse order.
The stabilizer of a partial flag obtained by forgetting some parts of the standard flag can be described as a set of block upper triangular matrices (but its elements are ''not'' all triangular matrices). The conjugates of such a group are the subgroups defined as the stabilizer of some partial flag. These subgroups are called parabolic subgroups.
=== Examples ===
The group of 2×2 upper unitriangular matrices is [[isomorphic]] to the [[Abelian group|additive group]] of the field of scalars; in the case of complex numbers it corresponds to a group formed of parabolic [[Möbius transformation]]s; the 3×3 upper unitriangular matrices form the [[Heisenberg group]].
== See also ==
Line 254 ⟶ 221:
* [[Tridiagonal matrix]]
* [[Invariant subspace]]
== References ==
{{reflist}}
{{Matrix classes}}
[[Category:Numerical linear algebra]]
[[Category:Matrices (mathematics)]]
| |||