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{{About|the expansion of the determinant of a square matrix as a weighted sum of determinants of sub-matrices|the expansion of an 1/r-potential using spherical harmonical functions|Laplace expansion (potential)}}
{{About|the expansion of the determinant of a square matrix as a weighted sum of determinants of sub-matrices|the expansion of an 1/r-potential using spherical harmonical functions|Laplace expansion (potential)}}


In [[linear algebra]], the '''Laplace expansion''', named after [[Pierre-Simon Laplace]], also called '''cofactor expansion''', is an expression for the [[determinant]] |''B''| of
In [[linear algebra]], the '''Laplace expansion''', named after [[Pierre-Simon Laplace]], also called '''cofactor expansion''', is an expression for the [[determinant]] |''B''| of an ''n'' × ''n'' [[Matrix (mathematics)|matrix]] ''B'' that is a weighted sum of the determinants of ''n'' sub-matrices of ''B'', each of size (''n''−1) × (''n''−1). The Laplace expansion is of theoretical interest as one of several ways to view the determinant, as well as of practical use in determinant computation.
an ''n'' × ''n'' square [[Matrix (mathematics)|matrix]] ''B'' that is a weighted sum of the determinants of ''n'' sub-matrices of ''B'', each of size (''n''−1) × (''n''−1). The Laplace expansion is of theoretical interest as one of several ways to view the determinant, as well as of practical use in determinant computation.


The ''i'', ''j'' ''[[cofactor (linear algebra)|cofactor]]'' of ''B'' is the scalar ''C<sub>ij</sub>'' defined by
The ''i'', ''j'' ''[[cofactor (linear algebra)|cofactor]]'' of ''B'' is the scalar ''C<sub>ij</sub>'' defined by

:<math>C_{ij}\ = (-1)^{i+j} M_{ij}\,,</math>
:<math>C_{ij}\ = (-1)^{i+j} M_{ij}\,,</math>

where ''M<sub>ij</sub>'' is the ''i'', ''j'' ''[[minor (linear algebra)|minor]] matrix'' of ''B'', that is, the determinant of the (''n''–1)&nbsp;&times;&nbsp;(''n''–1) matrix that results from deleting the ''i''-th row and the ''j''-th column of ''B''.
where ''M<sub>ij</sub>'' is the ''i'', ''j'' ''[[minor (linear algebra)|minor]] matrix'' of ''B'', that is, the determinant of the (''n'' − 1) × (''n'' − 1) matrix that results from deleting the ''i''-th row and the ''j''-th column of ''B''.


Then the Laplace expansion is given by the following
Then the Laplace expansion is given by the following


'''Theorem'''. Suppose ''B'' = (''b''<sub>''ij''</sub>) is an ''n''&nbsp;&times;&nbsp;''n'' matrix and fix any ''i'', ''j'' ∈ {1, 2, ..., ''n''}.
:'''Theorem'''. Suppose ''B'' = (''b''<sub>''ij''</sub>) is an ''n'' × ''n'' matrix and fix any ''i'', ''j'' ∈ {1, 2, ..., ''n''}.


Then its determinant |''B''| is given by:
Then its determinant |''B''| is given by:


:<math> \begin{align}|B| & {} = b_{i1} C_{i1} + b_{i2} C_{i2} + \cdots + b_{in} C_{in} \\ & {} = b_{1j} C_{1j} + b_{2j} C_{2j} + \cdots + b_{nj} C_{nj} \\
:<math> \begin{align}
|B| & = b_{i1} C_{i1} + b_{i2} C_{i2} + \cdots + b_{in} C_{in} \\
& = b_{1j} C_{1j} + b_{2j} C_{2j} + \cdots + b_{nj} C_{nj} \\
& {} = \sum_{j'=1}^{n} b_{ij'} C_{ij'} = \sum_{i'=1}^{n} b_{i'j} C_{i'j} . \end{align}</math>
& = \sum_{j'=1}^{n} b_{ij'} C_{ij'} = \sum_{i'=1}^{n} b_{i'j} C_{i'j}
\end{align}</math>


== Examples ==
== Examples ==
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==Proof==
==Proof==
Suppose <math>B</math> is an ''n''&nbsp;&times;&nbsp;''n'' matrix and <math>i,j\in\{1,2,\dots,n\}.</math> For clarity we also label the entries of <math>B</math> that compose its <math>i,j</math> minor matrix <math>M_{ij}</math> as
Suppose <math>B</math> is an ''n'' × ''n'' matrix and <math>i,j\in\{1,2,\dots,n\}.</math> For clarity we also label the entries of <math>B</math> that compose its <math>i,j</math> minor matrix <math>M_{ij}</math> as


<math>(a_{st})</math> for <math>1 \le s,t \le n-1.</math>
<math>(a_{st})</math> for <math>1 \le s,t \le n-1.</math>
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==Laplace expansion of a determinant by complementary minors==
==Laplace expansion of a determinant by complementary minors==

Laplaces cofactor expansion can be generalised as follows.
Laplaces cofactor expansion can be generalised as follows.


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Consider the matrix
Consider the matrix
:<math> A = \begin{bmatrix} 1 & 2 & 3 & 4 \\ 5 & 6 & 7 & 8 \\ 9 & 10 & 11 & 12 \\ 13 & 14 & 15 & 16 \end{bmatrix}. </math>
:<math> A = \begin{bmatrix} 1 & 2 & 3 & 4 \\ 5 & 6 & 7 & 8 \\ 9 & 10 & 11 & 12 \\ 13 & 14 & 15 & 16 \end{bmatrix}. </math>
The determinant of this matrix can be computed by using the Laplace's cofactor expansion along the first two rows as follows. Firstly note that there are 6 sets of two distinct numbers in <math>\left\{1,2,3,4\right\}</math>, namely let <math>S=\left\{\{1,2\},\{1,3\},\{1,4\},\{2,3\},\{2,4\},\{3,4\}\right\}</math> be the aformentioned set.
The determinant of this matrix can be computed by using the Laplace's cofactor expansion along the first two rows as follows. Firstly note that there are 6 sets of two distinct numbers in {{math|{1, 2, 3, 4},}} namely let <math>S=\left\{\{1,2\},\{1,3\},\{1,4\},\{2,3\},\{2,4\},\{3,4\}\right\}</math> be the aformentioned set.


By defining the complementary cofactors to be
By defining the complementary cofactors to be
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In our explicit example this gives us
In our explicit example this gives us

:<math> |A| = b_{\{1,2\}}c_{\{3,4\}}
:<math>\begin{align}
-b_{\{1,3\}}c_{\{2,4\}}
|A| &= b_{\{1,2\}}c_{\{3,4\}} -b_{\{1,3\}}c_{\{2,4\}} +b_{\{1,4\}}c_{\{2,3\}}+b_{\{2,3\}}c_{\{1,4\}} -b_{\{2,4\}}c_{\{1,3\}} +b_{\{3,4\}}c_{\{1,2\}} \\
+b_{\{1,4\}}c_{\{2,3\}}
&= \begin{vmatrix} 1 & 2 \\ 5 & 6 \end{vmatrix} \cdot \begin{vmatrix} 11 & 12 \\ 15 & 16 \end{vmatrix}
+b_{\{2,3\}}c_{\{1,4\}}
-b_{\{2,4\}}c_{\{1,3\}}
+b_{\{3,4\}}c_{\{1,2\}} </math>
:::<math> {} = \begin{vmatrix} 1 & 2 \\ 5 & 6 \end{vmatrix} \cdot \begin{vmatrix} 11 & 12 \\ 15 & 16 \end{vmatrix}
- \begin{vmatrix} 1 & 3 \\ 5 & 7 \end{vmatrix} \cdot \begin{vmatrix} 10 & 12 \\ 14 & 16 \end{vmatrix}
- \begin{vmatrix} 1 & 3 \\ 5 & 7 \end{vmatrix} \cdot \begin{vmatrix} 10 & 12 \\ 14 & 16 \end{vmatrix}
+ \begin{vmatrix} 1 & 4 \\ 5 & 8 \end{vmatrix} \cdot \begin{vmatrix} 10 & 11 \\ 14 & 15 \end{vmatrix}
+ \begin{vmatrix} 1 & 4 \\ 5 & 8 \end{vmatrix} \cdot \begin{vmatrix} 10 & 11 \\ 14 & 15 \end{vmatrix}
+ \begin{vmatrix} 2 & 3 \\ 6 & 7 \end{vmatrix} \cdot \begin{vmatrix} 9 & 12 \\ 13 & 16 \end{vmatrix}
+ \begin{vmatrix} 2 & 3 \\ 6 & 7 \end{vmatrix} \cdot \begin{vmatrix} 9 & 12 \\ 13 & 16 \end{vmatrix}
- \begin{vmatrix} 2 & 4 \\ 6 & 8 \end{vmatrix} \cdot \begin{vmatrix} 9 & 11 \\ 13 & 15 \end{vmatrix}
- \begin{vmatrix} 2 & 4 \\ 6 & 8 \end{vmatrix} \cdot \begin{vmatrix} 9 & 11 \\ 13 & 15 \end{vmatrix}
+ \begin{vmatrix} 3 & 4 \\ 7 & 8 \end{vmatrix} \cdot \begin{vmatrix} 9 & 10 \\ 13 & 14 \end{vmatrix} </math>
+ \begin{vmatrix} 3 & 4 \\ 7 & 8 \end{vmatrix} \cdot \begin{vmatrix} 9 & 10 \\ 13 & 14 \end{vmatrix}\\
&= -4 \cdot (-4) -(-8) \cdot (-8) +(-12) \cdot (-4) +(-4) \cdot (-12) -(-8) \cdot (-8) +(-4) \cdot (-4)\\
:::<math> {} = -4 \cdot (-4)
-(-8) \cdot (-8)
&= 16 - 64 + 48 + 48 - 64 + 16 = 0.
\end{align}</math>
+(-12) \cdot (-4)

+(-4) \cdot (-12)
-(-8) \cdot (-8)
+(-4) \cdot (-4) </math>
:::<math> {} = 16
- 64
+ 48
+ 48
- 64
+ 16 = 0. </math>
As above, It is easy to verify that the result is correct: the matrix is [[invertible matrix|singular]] because the sum of its first and third column is twice the second column, and hence its determinant is zero.
As above, It is easy to verify that the result is correct: the matrix is [[invertible matrix|singular]] because the sum of its first and third column is twice the second column, and hence its determinant is zero.


==Computational expense==
==Computational expense==
The Laplace expansion is computationally inefficient for high dimension because for ''N'' × ''N'' matrices, the computational effort scales with ''N''!. Therefore, the Laplace expansion is not suitable for large ''N''. Using a decomposition into [[triangular matrix|triangular matrices]] as in the [[LU decomposition]], one can determine determinants with effort ''N''<sup>3</sup>/3.<ref>Stoer Bulirsch: Introduction to Numerical Mathematics</ref>

The Laplace expansion is computationally inefficient for high dimension because for ''N''x''N'' matrices, the computational effort scales with ''N''!. Therefore, the Laplace expansion is not suitable for large ''N''. Using a decomposition into [[triangular matrix|triangular matrices]] as in the [[LU decomposition]], one can determine determinants with effort ''N''<sup>3</sup>/3.<ref>Stoer Bulirsch: Introduction to Numerical Mathematics</ref>


==References==
==References==

<references />
<references />
*David Poole: ''Linear Algebra. A Modern Introduction''. Cengage Learning 2005, ISBN 0-534-99845-3, p. 265-267 ({{Google books|oBk3u2fDFc8C|restricted online copy|page=265}})
*David Poole: ''Linear Algebra. A Modern Introduction''. Cengage Learning 2005, ISBN 0-534-99845-3, p. 265-267 ({{Google books|oBk3u2fDFc8C|restricted online copy|page=265}})

Revision as of 05:37, 31 January 2015

In linear algebra, the Laplace expansion, named after Pierre-Simon Laplace, also called cofactor expansion, is an expression for the determinant |B| of an n × n matrix B that is a weighted sum of the determinants of n sub-matrices of B, each of size (n−1) × (n−1). The Laplace expansion is of theoretical interest as one of several ways to view the determinant, as well as of practical use in determinant computation.

The i, j cofactor of B is the scalar Cij defined by

where Mij is the i, j minor matrix of B, that is, the determinant of the (n − 1) × (n − 1) matrix that results from deleting the i-th row and the j-th column of B.

Then the Laplace expansion is given by the following

Theorem. Suppose B = (bij) is an n × n matrix and fix any i, j ∈ {1, 2, ..., n}.

Then its determinant |B| is given by:

Examples

Consider the matrix

The determinant of this matrix can be computed by using the Laplace expansion along any one of its rows or columns. For instance, an expansion along the first row yields:

Laplace expansion along the second column yields the same result:

It is easy to verify that the result is correct: the matrix is singular because the sum of its first and third column is twice the second column, and hence its determinant is zero.

Proof

Suppose is an n × n matrix and For clarity we also label the entries of that compose its minor matrix as

for

Consider the terms in the expansion of that have as a factor. Each has the form

for some permutation τ ∈ Sn with , and a unique and evidently related permutation which selects the same minor entries as Similarly each choice of determines a corresponding i.e. the correspondence is a bijection between and The permutation can be derived from as follows.

Define by for and . Then and

Since the two cycles can be written respectively as and transpositions,

And since the map is bijective,

from which the result follows.

Laplace expansion of a determinant by complementary minors

Laplaces cofactor expansion can be generalised as follows.

Example

Consider the matrix

The determinant of this matrix can be computed by using the Laplace's cofactor expansion along the first two rows as follows. Firstly note that there are 6 sets of two distinct numbers in {1, 2, 3, 4}, namely let be the aformentioned set.

By defining the complementary cofactors to be

,
,

and the sign of their permutation to be

.

The determinant of A can be written out as

where is the complementary set to .

In our explicit example this gives us

As above, It is easy to verify that the result is correct: the matrix is singular because the sum of its first and third column is twice the second column, and hence its determinant is zero.

Computational expense

The Laplace expansion is computationally inefficient for high dimension because for N × N matrices, the computational effort scales with N!. Therefore, the Laplace expansion is not suitable for large N. Using a decomposition into triangular matrices as in the LU decomposition, one can determine determinants with effort N3/3.[1]

References

  1. ^ Stoer Bulirsch: Introduction to Numerical Mathematics

See also