一个排列 是一个从一个集合到自身的双射函数。
设
是一个有限集合。函数
被称为一个排列当且仅当 它是一对一并且满射。
也就是说,对于所有的
存在唯一的
使得
.
所有
元素的排列的集合用
表示。
对于
有
种不同的排列
![{\displaystyle {\begin{aligned}&\sigma _{1}={\begin{bmatrix}1&2&3\\1&2&3\end{bmatrix}},\quad \sigma _{2}={\begin{bmatrix}1&2&3\\1&3&2\end{bmatrix}}\\[5pt]&\sigma _{3}={\begin{bmatrix}1&2&3\\2&1&3\end{bmatrix}},\quad \sigma _{4}={\begin{bmatrix}1&2&3\\2&3&1\end{bmatrix}}\\[5pt]&\sigma _{5}={\begin{bmatrix}1&2&3\\3&1&2\end{bmatrix}},\quad \sigma _{6}={\begin{bmatrix}1&2&3\\3&2&1\end{bmatrix}}\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/ab3de4604f4ef8315c4360c299b83945015adf6c)
一般而言,如果
那么
.
令
为一个多项式。我们定义

令
为多项式。然后我们有
其中
.



- 根据定义,置换仅作用于变量索引。
- 首先,令
为以下形式的单项式:
![{\displaystyle {\begin{aligned}F&=a\,X_{1}^{a_{1}}\!\cdots X_{n}^{a_{n}}\\[5pt]G&=b\,X_{1}^{b_{1}}\!\cdots X_{n}^{b_{n}}\\[5pt]\sigma (F\pm G)&=\sigma {\bigl (}a\,X_{1}^{a_{1}}\!\cdots X_{n}^{a_{n}}\!\pm b\,X_{1}^{b_{1}}\!\cdots X_{n}^{b_{n}}{\bigl )}\\[5pt]&=a\,X_{\sigma (1)}^{a_{1}}\!\cdots X_{\sigma (n)}^{a_{n}}\!\pm b\,X_{\sigma (1)}^{b_{1}}\!\cdots X_{\sigma (n)}^{b_{n}}\\[5pt]&=\sigma {\bigl (}a\,X_{1}^{a_{1}}\!\cdots X_{n}^{a_{n}}{\bigl )}\pm \sigma {\bigl (}b\,X_{1}^{b_{1}}\!\cdots X_{n}^{b_{n}}{\bigl )}\\[5pt]&=\sigma (F)\pm \sigma (G)\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/cb51dce96e5147312eaf2a5cafe8cce2f0ba01a8)
- 我们可以通过归纳法 对
进行推广,使得
为单项式。
- 与之前相同,令
为以下形式的单项式:
![{\displaystyle {\begin{aligned}F&=a\,X_{1}^{a_{1}}\!\cdots X_{n}^{a_{n}}\\[5pt]G&=b\,X_{1}^{b_{1}}\!\cdots X_{n}^{b_{n}}\\[5pt]\sigma (F\cdot G)&=\sigma {\bigl (}a\,X_{1}^{a_{1}}\!\cdots X_{n}^{a_{n}}\!\cdot b\,X_{1}^{b_{1}}\!\cdots X_{n}^{b_{n}}{\bigl )}\\[5pt]&=ab\,\sigma {\bigl (}X_{1}^{a_{1}+b_{1}}\!\cdots X_{n}^{a_{n}+b_{n}}{\bigl )}\\[5pt]&=ab\,X_{\sigma (1)}^{a_{1}+b_{1}}\!\cdots X_{\sigma (n)}^{a_{n}+b_{n}}\\[5pt]&={\bigl (}a\,X_{\sigma (1)}^{a_{1}}\!\cdots X_{\sigma (n)}^{a_{n}}{\bigr )}\cdot {\bigl (}b\,X_{\sigma (1)}^{b_{1}}\!\cdots X_{\sigma (n)}^{b_{n}}{\bigr )}\\[5pt]&=\sigma {\bigl (}a\,X_{1}^{a_{1}}\!\cdots X_{n}^{a_{n}}{\bigl )}\cdot \sigma {\bigl (}b\,X_{1}^{b_{1}}\!\cdots X_{n}^{b_{n}}{\bigl )}\\[5pt]&=\sigma (F)\cdot \sigma (G)\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/222c0976edf26bb60bee9266099a42dde82a7b4a)
- 再次,我们可以通过归纳法对
进行推广,使得
是单项式![{\displaystyle {\begin{aligned}\sigma (F\cdot G)&=\sigma {\bigg (}\sum _{i_{1}\,=\,1}^{k_{1}}F_{i_{1}}\cdot \sum _{i_{2}\,=\,1}^{k_{2}}G_{i_{2}}{\bigg )}\\[5pt]&=\sigma {\bigg (}\sum _{i_{1}\,=\,1}^{k_{1}}\sum _{i_{2}\,=\,1}^{k_{2}}F_{i_{1}}G_{i_{2}}{\bigg )}\\[5pt]&=\sum _{i_{1}\,=\,1}^{k_{1}}\sum _{i_{2}\,=\,1}^{k_{2}}\sigma (F_{i_{1}}\!\cdot G_{i_{2}})\\[5pt]&=\sum _{i_{1}\,=\,1}^{k_{1}}\sum _{i_{2}\,=\,1}^{k_{2}}\sigma (F_{i_{1}})\cdot \sigma (G_{i_{2}})\\[5pt]&=\sum _{i_{1}\,=\,1}^{k_{1}}\sigma (F_{i_{1}})\cdot \sum _{i_{2}\,=\,1}^{k_{2}}\sigma (G_{i_{2}})\\&=\sigma {\bigg (}\sum _{i_{1}\,=\,1}^{k_{1}}F_{i_{1}}{\bigg )}\cdot \sigma {\bigg (}\sum _{i_{2}\,=\,1}^{k_{2}}G_{i_{2}}{\bigg )}\\[5pt]&=\sigma (F)\cdot \sigma (G)\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/ba125ff6b5150fe677961371e866483048352ec4)
![{\displaystyle {\begin{aligned}(\sigma _{1}\circ \sigma _{2})(F)&=F{\bigl (}X_{(\sigma _{1}\,\circ \,\sigma _{2})(1)},\ldots ,X_{(\sigma _{1}\,\circ \,\sigma _{2})(n)}{\bigr )}\\[5pt]&=F{\bigl (}X_{\sigma _{1}(\sigma _{2}(1))},\ldots ,X_{\sigma _{1}(\sigma _{2}(n))}{\bigr )}\\[5pt]&=\sigma _{1}{\bigl (}F(X_{\sigma _{2}(1)},\ldots ,X_{\sigma _{2}(n)}){\bigr )}\\[5pt]&=\sigma _{1}(\sigma _{2}(F))\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/710ec98971c6c6d524692bb58122efa3558cabc3)
令
为多项式。如果

对于所有排列
。则称之为 对称 多项式。
![{\displaystyle {\begin{aligned}P({\color {red}x_{1}},{\color {Green}x_{2}},{\color {blue}x_{3}})&={\color {red}x_{1}^{2}}{\color {Green}x_{2}^{2}}{\color {blue}x_{3}^{2}}+{\color {red}3x_{1}}+{\color {Green}3x_{2}}+{\color {blue}3x_{3}}\\[5pt]&={\color {red}x_{1}^{2}}{\color {blue}x_{3}^{2}}{\color {Green}x_{2}^{2}}+{\color {red}3x_{1}}+{\color {blue}3x_{3}}+{\color {Green}3x_{2}}\\[5pt]&={\color {Green}x_{2}^{2}}{\color {red}x_{1}^{2}}{\color {blue}x_{3}^{2}}+{\color {Green}3x_{2}}+{\color {red}3x_{1}}+{\color {blue}3x_{3}}\\[5pt]&={\color {Green}x_{2}^{2}}{\color {blue}x_{3}^{2}}{\color {red}x_{1}^{2}}+{\color {Green}3x_{2}}+{\color {blue}3x_{3}}+{\color {red}3x_{1}}\\[5pt]&={\color {blue}x_{3}^{2}}{\color {red}x_{1}^{2}}{\color {Green}x_{2}^{2}}+{\color {blue}3x_{3}}+{\color {red}3x_{1}}+{\color {Green}3x_{2}}\\[5pt]&={\color {blue}x_{3}^{2}}{\color {Green}x_{2}^{2}}{\color {red}x_{1}^{2}}+{\color {blue}3x_{3}}+{\color {Green}3x_{2}}+{\color {red}3x_{1}}\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/ac511dfb498833bb30d5c5fe4dc212410e32d1db)
![{\displaystyle {\begin{aligned}P({\color {red}x_{1}},{\color {Green}x_{2}},{\color {blue}x_{3}})&={\color {red}x_{1}}+{\color {Green}x_{2}}-{\color {blue}x_{3}}\\[5pt]&\neq {\color {red}x_{1}}+{\color {blue}x_{3}}-{\color {Green}x_{2}}\\[5pt]&\neq {\color {Green}x_{2}}+{\color {red}x_{1}}-{\color {blue}x_{3}}\\[5pt]&\neq {\color {Green}x_{2}}+{\color {blue}x_{3}}-{\color {red}x_{1}}\\[5pt]&\neq {\color {blue}x_{3}}+{\color {red}x_{1}}-{\color {Green}x_{2}}\\[5pt]&\neq {\color {blue}x_{3}}+{\color {Green}x_{2}}-{\color {red}x_{1}}\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/b56adf83a86a80d88289d3bb0be503e6b984d900)
- 对称多项式的和与积也是对称多项式。
- 设
是变量
的多项式,设
是变量
的对称多项式。
- 那么
在变量
中也是对称的。
- 这由定义 2 中的性质和上面对称多项式的定义得出。
- 根据定义,我们得到
![{\displaystyle {\begin{aligned}\sigma {\bigl (}F({\vec {G}}{}^{m}({\vec {X}}{}^{n})){\bigr )}&=\sigma {\bigl (}F(G_{1}({\vec {X}}{}^{n}),\ldots ,G_{m}({\vec {X}}{}^{n})){\bigr )}\\[5pt]&=F{\bigl (}\sigma (G_{1}({\vec {X}}{}^{n})),\ldots ,\sigma (G_{m}({\vec {X}}{}^{n})){\bigr )}\\[5pt]&=F{\bigl (}G_{1}({\vec {X}}{}^{n}),\ldots ,G_{m}({\vec {X}}{}^{n}){\bigr )}\\[5pt]&=F({\vec {G}}{}^{m}({\vec {X}}{}^{n}))\end{aligned}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/e4fcb0622503c3622f11862b3f785a6a8dfcd0f4)