AntihermitianMatrixQ
更多信息和选项
- AntihermitianMatrixQ 也被称为斜埃尔米特矩阵.
- 如果 m-ConjugateTranspose[m],则矩阵 m 为反埃尔米特矩阵.
- AntihermitianMatrixQ 适用于符号和数值矩阵.
- 可以给出下列选项:
-
SameTest Automatic 检验表达式相等性的函数 Tolerance Automatic 数值近似的偏差 - 对于精确和符号矩阵,如果 f[mij,mkl] 给出 True,选项 SameTest->f 表示 mij 和 mkl 这两个项相等.
- 对于近似矩阵,可以使用选项 Tolerance->t 表示所有的项 Abs[mij]≤t 均为零.
- 对于矩阵的项 Abs[mij]>t,都进行相等性比较,除最后一个
位外,其中对于 MachinePrecision 矩阵,
为 $MachineEpsilon,对于 Precision 为
的矩阵,
为
.
范例
打开所有单元 关闭所有单元基本范例 (2)
范围 (10)
基础用法 (6)
m = {{0, -2.3}, {2.3, 0}};AntihermitianMatrixQ[m]AntisymmetricMatrixQ[m]m = {{I, 2 - 3I}, {-2 - 3I, -I}};AntihermitianMatrixQ[m]{AntisymmetricMatrixQ[Re[m]], SymmetricMatrixQ[Im[m]]}m = (| | | |
| - | -- | -- |
| 4 | -5 | 2 |
| 3 | -3 | -3 |
| 5 | 5 | 5 |);AntihermitianMatrixQ[m]AntihermitianMatrixQ[(m - m^/2)]将 AntihermitianMatrixQ 用于任意精度矩阵:
m = RandomReal[5, {3, 3}, WorkingPrecision -> 15]AntihermitianMatrixQ[m]将 AntihermitianMatrixQ 用于符号矩阵:
AntihermitianMatrixQ[{{a, b}, {c, d}}]当 c=-b 且 a 和 d 的实部为零时,该矩阵变为反埃尔米特矩阵:
Block[{c = -b^, a = I Re[e], d = I Re[f]}, AntihermitianMatrixQ[{{a, b}, {c, d}}]]AntihermitianMatrixQ 可高效地处理大型数值矩阵:
m = RandomReal[1, {1000, 1000}];AbsoluteTiming[AntihermitianMatrixQ[m]]a = m - m^;
AbsoluteTiming[AntihermitianMatrixQ[a]]特殊矩阵 (4)
将 AntihermitianMatrixQ 用于稀疏矩阵:
SparseArray[{i_, j_} -> i - j + (i + j)I, {5, 5}]AntihermitianMatrixQ[%]将 AntihermitianMatrixQ 用于结构化矩阵:
SymmetrizedArray[{{1, 2} -> 4, {3, 2} -> 5}, {3, 3}, Antisymmetric[All]]AntihermitianMatrixQ[%]用于 QuantityArray 结构化矩阵:
QuantityArray[{{1, 2}, {2, 5}}, "Meters"]AntihermitianMatrixQ[%]AntihermitianMatrixQ[IdentityMatrix[3]]AntihermitianMatrixQ[IdentityMatrix[3]I]HilbertMatrix 不是反埃尔米特矩阵:
AntihermitianMatrixQ[HilbertMatrix[5]]选项 (2)
SameTest (1)
对于正实数
,矩阵为反埃尔米特矩阵,但 AntihermitianMatrixQ 给出 False:
m = {{I, Exp[Log[I x]]}, {I x, 2I}};AntihermitianMatrixQ[m]使用选项 SameTest 得到正确的答案:
AntihermitianMatrixQ[m, SameTest -> (Simplify[#1 - #2, x > 0] == 0&)]Tolerance (1)
SeedRandom[56];
m = Block[{a = RandomComplex[1 + I, {3, 3}]}, a - a^];
m = m + 10^-14 RandomReal[10, {3, 3}];AntihermitianMatrixQ[m]调整选项 Tolerance,接受这个矩阵为反埃尔米特矩阵:
AntihermitianMatrixQ[m, Tolerance -> 10 ^ -12]m + m^//Norm应用 (6)
f[x_, y_] := Re[x]Re[y] IConjugate[f[y, x]] == -f[x, y]//Simplify使用 Table 生成反埃尔米特矩阵:
AntihermitianMatrixQ[Table[f[i, j], {i, 5}, {j, 5}]]SymmetrizedArray 可以生成具有对称性的矩阵(和一般数组):
SymmetrizedArray[{{1, 2} -> a, {2, 3} -> b}, {3, 3}, Antihermitian[{1, 2}]]AntihermitianMatrixQ[%]使用 Normal 转换回普通矩阵:
Normal[SymmetrizedArray[StructuredArray`StructuredData[{3, 3}, {{{1, 2} -> a, {2, 3} -> b},
Antihermitian[{1, 2}]}]]]r[θ_] := (| | |
| ------ | ------- |
| Cos[θ] | -Sin[θ] |
| Sin[θ] | Cos[θ] |)D[r[θ], θ].Inverse[r[θ]]//FullSimplifyAntihermitianMatrixQ[%]rot = RotationMatrix[θ, RandomReal[1, 3, WorkingPrecision -> 5]];
AntisymmetricMatrixQ[D[rot, θ].Inverse[rot]]斯通定理阐述,任何单参数的酉矩阵系列都有一个反埃尔米特对数导数. 验证以下矩阵系列的定理:
u[t_] := (| | |
| -------- | -------- |
| Cos[t] | I Sin[t] |
| I Sin[t] | Cos[t] |)UnitaryMatrixQ[u[t], SameTest -> (Simplify[#1 - #2 == 0, t∈Reals] &)]u'[t].Inverse[u[t]]//FullSimplifyAntihermitianMatrixQ[%]在量子力学中,时间演化由酉矩阵的单参数系列
表示.
乘以
的对数导数是称为哈密顿量或能量算子
的埃尔米特矩阵. 其特征值代表系统的可能能量. 对于以下时间演化,计算哈密顿量和可能的能量:
u[t_] := (| | | |
| ------------------------ | ---------------------- | ------------------------ |
| (1/2) + (1/2) Cos[t ω0] | -(I Sin[t ω0]/Sqrt[2]) | -(1/2) + (1/2) Cos[t ω0] |
| -(I Sin[t ω0]/Sqrt[2]) | Cos[t ω0] | -(I Sin[t ω0]/Sqrt[2]) |
| -(1/2) + (1/2) Cos[t ω0] | -(I Sin[t ω0]/Sqrt[2]) | (1/2) + (1/2) Cos[t ω0] |)UnitaryMatrixQ[u[t], SameTest -> (Simplify[#1 - #2 == 0, {t, Subscript[ω, 0]}∈Reals] &)]u'[t].Inverse[u[t]]//FullSimplifyAntihermitianMatrixQ[%, SameTest -> (Simplify[#1 - #2 == 0, {Subscript[ω, 0]}∈Reals] &)]MatrixForm[ℋ = I ℏ %%]HermitianMatrixQ[ℋ, SameTest -> (Simplify[#1 - #2 == 0, {ℏ, Subscript[ω, 0]}∈Reals] &)]Eigenvalues[ℋ]反埃尔米特矩阵
的 MatrixExp[v] 是酉矩阵. 通过其微分方程
定义矩阵函数,其中初始值为
,表明解是酉型的:
AntihermitianMatrixQ[v = (| | | |
| ------- | ----- | ------ |
| 2I | -0.5I | 1. - I |
| -0.5I | 0 | 1. |
| -1. - I | -1. | -I |)]mm = NDSolveValue[{m'[t] == v.m[t], m[0] == IdentityMatrix[3]}, m, {t, 0., 5.}]Table[UnitaryMatrixQ[mm[t], Tolerance -> 10 ^ -5], {t, 0., 5., 1.}]Table[Norm@mm[t], {t, 0., 5., 1.}]rows = Table[ParametricPlot3D[Abs[mm[t][[i]]], {t, 0., 5.}, PlotStyle -> ColorData[1, i]], {i, 3}]//Quiet;Show[Graphics3D[{Opacity[0.5], Sphere[]}], rows]属性和关系 (15)
对于任何不是矩阵的 x,AntihermitianMatrixQ[x] 都会返回 False:
AntihermitianMatrixQ[Sqrt[3]]如果 m==-ConjugateTranspose[m],则矩阵为反埃尔米特矩阵:
m = (| | | |
| ------- | ------ | --- |
| 2I | 2 + 2I | -3I |
| -2 + 2I | -I | 4 |
| -3I | -4 | 0 |);{m == -ConjugateTranspose[m], AntihermitianMatrixQ[m]}m = (| | |
| -------- | ------- |
| I | 2 + 2 I |
| -2 + 2 I | 0 |);AntihermitianMatrixQ[m]使用 Diagonal 可选出对角线元素:
Diagonal[m]m = (| | |
| -- | - |
| 0 | 2 |
| -2 | 0 |);{AntisymmetricMatrixQ[m], AntihermitianMatrixQ[m]}m = (| | |
| -------- | ------- |
| 0 | 2 + 2 I |
| -2 - 2 I | 0 |);{AntisymmetricMatrixQ[m], AntihermitianMatrixQ[m]}使用 Symmetrize 和对称 Antihermitian 来计算矩阵的反埃尔米特部分:
m = RandomComplex[1 + I, {3, 3}];Symmetrize[m, Antihermitian[{1, 2}]]这等于 m 和 ConjugateTranspose[m] 之间的归一化差:
% == (m - m/2)m = RandomComplex[1 + I, {3, 3}];
{hr, ahr} = {(m + m^) / 2, (m - m^) / 2};m == hr + ahr使用 HermitianMatrixQ 检验矩阵是否是埃尔米特矩阵:
{HermitianMatrixQ[hr], AntihermitianMatrixQ[ahr]}m = Block[{a = RandomComplex[1 + I, {5, 5}]}, a + a^];{HermitianMatrixQ[m], AntihermitianMatrixQ[I m]}反埃尔米特矩阵 m 的 MatrixExp[m] 是酉矩阵:
m = Block[{a = RandomComplex[1 + I, {5, 5}]}, a - a^];UnitaryMatrixQ[MatrixExp[m]]m = Block[{a = RandomComplex[1 + I, {5, 5}]}, a - a^];使用 NormalMatrixQ 检验矩阵是否为正规矩阵:
{AntihermitianMatrixQ[m], NormalMatrixQ[m]}m = Block[{a = RandomComplex[1 + I, {4, 4}]}, a - a^];AntihermitianMatrixQ[m]使用 Eigenvalues 求特征值:
Eigenvalues[m]//Chop反埃尔米特矩阵 m 的 CharacteristicPolynomial[m,x] 交替实部和虚部系数:
m = Block[{a = RandomComplex[1 + I, {4, 4}]}, a - a^];CharacteristicPolynomial[m, x]//Chopm = Block[{a = RandomComplex[1 + I, {3, 3}]}, a - a^];AntihermitianMatrixQ[m]DiagonalizableMatrixQ[m]使用 Eigenvectors 求特征向量:
Eigenvectors[m]奇数维反对称矩阵 m 的 Det[m] 是虚数:
m = Block[{a = RandomComplex[1 + I, {5, 5}]}, a - a^];{AntihermitianMatrixQ[m], Det[m]//Chop}m = Block[{a = RandomComplex[1 + I, {4, 4}]}, a - a^];{AntihermitianMatrixQ[m], Det[m]//Chop}m = Block[{a = RandomComplex[1 + I, {3, 3}]}, a - a];AntihermitianMatrixQ[Inverse[m]]m = Block[{a = RandomComplex[1 + I, {3, 3}]}, a - a];AntihermitianMatrixQ[MatrixPower[m, 9]]HermitianMatrixQ[MatrixPower[m, 8]]相关指南
-
▪
- 矩阵判断
文本
Wolfram Research (2014),AntihermitianMatrixQ,Wolfram 语言函数,https://reference.wolfram.com/language/ref/AntihermitianMatrixQ.html.
CMS
Wolfram 语言. 2014. "AntihermitianMatrixQ." Wolfram 语言与系统参考资料中心. Wolfram Research. https://reference.wolfram.com/language/ref/AntihermitianMatrixQ.html.
APA
Wolfram 语言. (2014). AntihermitianMatrixQ. Wolfram 语言与系统参考资料中心. 追溯自 https://reference.wolfram.com/language/ref/AntihermitianMatrixQ.html 年
BibTeX
@misc{reference.wolfram_2026_antihermitianmatrixq, author="Wolfram Research", title="{AntihermitianMatrixQ}", year="2014", howpublished="\url{https://reference.wolfram.com/language/ref/AntihermitianMatrixQ.html}", note=[Accessed: 10-August-2026]}
BibLaTeX
@online{reference.wolfram_2026_antihermitianmatrixq, organization={Wolfram Research}, title={AntihermitianMatrixQ}, year={2014}, url={https://reference.wolfram.com/language/ref/AntihermitianMatrixQ.html}, note=[Accessed: 10-August-2026]}