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行列式

Last updated at Posted at 2020-04-02

行列式

行列式を求めたい理由

スクリーンショット 2020-03-11 19.10.17.png

行列式の定義

\begin{align}
 | A | = \mathrm{ det }A = \mathrm{ det }( a_1, a_2, \ldots, a_n ) =
\left|
  \begin{array}{cccc}
    a_{ 11 } & a_{ 12 } & \ldots & a_{ 1n } \\
    a_{ 21 } & a_{ 22 } & \ldots & a_{ 2n } \\
    \vdots & \vdots & \ddots & \vdots \\
    a_{ m1 } & a_{ m2 } & \ldots & a_{ mn }
  \end{array}
\right|



& = \sum_{ \sigma \in S_n  } sgn\ \sigma \cdot {x_{1\sigma_\left( 1\right)}}{x_{2\sigma_\left( 2\right)}}\cdots{x_{n\sigma_\left( n\right)}} \\

& = \sum_{ \sigma \in S_n  } sgn\ \sigma \prod_{ i = 1 }^n x_{i\sigma_\left( i\right)}
\end{align}

記号の意味

記号 意味
$ \displaystyle \sum_{ \sigma \in S_n }$ $n$ 文字に対する、あらゆる置換 $\sigma$ を考慮した和
$ \large{ x_{i\sigma_(i)}} $ 前半の 添え字 $i$ は 置換 $\sigma$ に依存していない
後半の添え字は $\sigma_{(i)}$ は $i$ に依存しており 置換 $\sigma$ による $i$ 番目の文字の移動先のインデックス番号。
置換は$ n-1 $ パターン存在する
$ \large{ sgn } $ sgn について

即ち、行列式とは

\begin{eqnarray}
A = \left(
  \begin{array}{cccc}
    a_{ 11 } & a_{ 12 } & \ldots & a_{ 1n } \\
    a_{ 21 } & a_{ 22 } & \ldots & a_{ 2n } \\
    \vdots & \vdots & \ddots & \vdots \\
    a_{ m1 } & a_{ m2 } & \ldots & a_{ mn }
  \end{array}
\right)
\end{eqnarray}

で表される行列$A$に対し
各行から列インデックスが被らないように要素をあらゆるパターンで抽出($\sigma \in S_n$した積の和(但し、積の係数は$sgn$)と言える

2次正方行列の場合

\begin{align}
|A| = \begin{vmatrix} {a_{11}} & {a_{12}} \\\ {a_{21}} & {a_{22}} \end{vmatrix} 
\end{align}

$ \sigma $ は $ n = 2$文字に対する置換であり、 $\sigma_1$ と $\sigma_2$ の2種類ある。即ち

\begin{eqnarray}
\sigma_1 & = \left(
  \begin{array}{cccc}
     1 & 2 \\
    1 & 2 
  \end{array}
\right)
,
sgn\ \sigma_1 = 1
\end{eqnarray}
\begin{eqnarray}

\sigma_2 = \left(
  \begin{array}{cccc}
     1 & 2 \\
    2 & 1 
  \end{array}
\right)
,
sgn\ \sigma_2 = -1
\end{eqnarray}

であり

\begin{align}
 | A | & = \sum_{ \sigma \in S_2  } sgn\ \sigma \cdot {a_{1\sigma_\left( 1\right)}}{a_{2\sigma_\left( 2\right)}} \\

& = sgn\ \sigma_1 \cdot a_{1\sigma_{1{(1)}}}a_{2\sigma_{1{(2)}}} + sgn\ \sigma_2 \cdot a_{1\sigma_{2{(1)}}}a_{2\sigma_{2{(2)}}} \\

& = {a_{11}}{a_{22} - {a_{12}} {a_{21}}}

\end{align}

3次正方行列の場合

\begin{align}
|A| = \begin{vmatrix}
{a_{11}} & {a_{12}} & {a_{13}} \\\ 
{a_{21}} & {a_{22}} & {a_{23}} \\\ 
{a_{31}} & {a_{32}} & {a_{33}} \end{vmatrix}
\end{align}

$ \sigma $ は $ n = 3$文字に対する置換であり、 $\sigma_1$, $\sigma_2$, $\sigma_3$, $\sigma_4$, $\sigma_5$, $\sigma_6$ の6 (=$3!$) 種類あり

\begin{eqnarray}
\sigma_1 & = \left(
  \begin{array}{cccc}
     1 & 2 & 3\\
    1 & 2 & 3
  \end{array}
\right)
,
sgn\ \sigma_1 = 1
\end{eqnarray}
\begin{eqnarray}
\sigma_2 = \left(
  \begin{array}{cccc}
     1 & 2 & 3 \\
     2 & 3 & 1
  \end{array}
\right)
,
sgn\ \sigma_2 = 1
\end{eqnarray}
\begin{eqnarray}
\sigma_3 = \left(
  \begin{array}{cccc}
     1 & 2 & 3 \\
     3 & 1 & 2
  \end{array}
\right)
,
sgn\ \sigma_3 = 1
\end{eqnarray}
\begin{eqnarray}
\sigma_4 = \left(
  \begin{array}{cccc}
     1 & 2 & 3 \\
     1 & 3 & 2
  \end{array}
\right)
,
sgn\ \sigma_4 = -1
\end{eqnarray}
\begin{eqnarray}
\sigma_5 = \left(
  \begin{array}{cccc}
     1 & 2 & 3 \\
     2 & 1 & 3
  \end{array}
\right)
,
sgn\ \sigma_5 = -1
\end{eqnarray}
\begin{eqnarray}
\sigma_6 = \left(
  \begin{array}{cccc}
     1 & 2 & 3 \\
     3 & 2 & 1
  \end{array}
\right)
,
sgn\ \sigma_6 = -1
\end{eqnarray}

であり

\begin{align}
 | A | & = \sum_{ \sigma \in S_2  } sgn\ \sigma \cdot {a_{1\sigma_\left( 1\right)}}{a_{2\sigma_\left( 2\right)}}{a_{3\sigma_\left( 3\right)}}{a_{4\sigma_\left( 4\right)}}{a_{5\sigma_\left( 5\right)}}{a_{6\sigma_\left( 6\right)}} \\

& = sgn\ \sigma_1 \cdot a_{1\sigma_{1{(1)}}}a_{2\sigma_{1{(2)}}}a_{3\sigma_{1{(2)}}} + \cdots + sgn\ \sigma_6 \cdot a_{1\sigma_{6{(1)}}}a_{2\sigma_{6{(2)}}}a_{2\sigma_{6{(2)}}} \\

& = {a_{11}}{a_{22}}{a_{33}} + {a_{12}}{a_{23}}{a_{31}} + {a_{13}}{a_{21}}{a_{32}} - {a_{11}}{a_{23}}{a_{32}} - {a_{12}}{a_{21}}{a_{33}} - {a_{13}}{a_{22}}{a_{31}}
\end{align}

参照

線形代数入門(斎藤 正彦 著)
行列式の性質
余因子展開

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