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Mathematics · 术语表

什么是 Annihilator?

Definition 2.5 University Mathematics — Year 2 · 第 2 章 — Linear Algebra

For a subspace F⊆EF \subseteq E, the annihilator is

F∘={φ∈E∗:φ∣F=0},F^{\circ} = \{\varphi \in E^* : \varphi|_F = 0\},

a subspace of E∗E^*.

示例

Example 2.7 (An annihilator, both directions)

Let F=Vect⁡((1,2,1), (1,0,−1))⊆R3F = \operatorname{Vect}\bigl((1, 2, 1),\ (1, 0, -1)\bigr) \subseteq \R^3. A form φ=a e1∗+b e2∗+c e3∗\varphi = a\,e_1^* + b\,e_2^* + c\,e_3^* annihilates FF iff

a+2b+c=0anda−c=0,a + 2b + c = 0 \qquad\text{and}\qquad a - c = 0 ,

i.e. c=ac = a and b=−ab = -a: F∘=R (e1∗−e2∗+e3∗)F^\circ = \R\,(e_1^* - e_2^* + e_3^*), of dimension 3−2=13 - 2 = 1 as Theorem 2.6 requires. Reading it backwards: F={(x,y,z):x−y+z=0}F = \{(x, y, z) : x - y + z = 0\} — the plane recovered as the kernel of the single form spanning F∘F^\circ. Going from a spanning family to equations is computing an annihilator; going from equations to a parametrization is computing a pre-annihilator. (Check: both spanning vectors satisfy x−y+z=0x - y + z = 0.)

Example 2.11 (Rank read on both sides)

Let

A=(120101111312).A = \begin{pmatrix} 1 & 2 & 0 & 1\\ 0 & 1 & 1 & 1\\ 1 & 3 & 1 & 2 \end{pmatrix} .

Column rank: the third row is the sum of the first two, so rk⁡A≤2\operatorname{rk} A \leq 2; columns 11 and 22 are free: rk⁡A=2\operatorname{rk} A = 2. The transpose’s kernel: solving ATy=0A^{\mathsf T}y = 0 gives y∈R (1,1,−1)y \in \R\,(1, 1, -1), so ker⁡AT\ker A^{\mathsf T} has dimension 1=3−21 = 3 - 2: exactly (im⁡A)∘(\operatorname{im} A)^\circ under the identification of (R3)∗(\R^3)^* with row vectors, as Proposition 2.10 asserts — the single relation “row3_3 = row1_1 + row2_2” is the annihilator of the column space. Row rank (22 free rows) and column rank agree not by accident but because both equal rk⁡A=rk⁡AT\operatorname{rk} A = \operatorname{rk} A^{\mathsf T}.

Example 2.24 (The trace pairing splits the matrix space)

On M2(R)\mathcal{M}_2(\R) with the pairing ⟨A,B⟩=tr⁡(AB)\langle A, B\rangle = \operatorname{tr}(AB) of Exercise 2.9: decompose M=(1423)M = \left(\begin{smallmatrix}1 & 4\\ 2 & 3\end{smallmatrix}\right) into symmetric and antisymmetric parts,

M=S+A,S=12(M+MT)=(1333),A=12(M−MT)=(01−10).M = S + A, \qquad S = \tfrac12(M + M^{\mathsf T}) = \begin{pmatrix}1 & 3\\ 3 & 3\end{pmatrix}, \qquad A = \tfrac12(M - M^{\mathsf T}) = \begin{pmatrix}0 & 1\\ -1 & 0\end{pmatrix}.

Then tr⁡(SA)=tr⁡(−31−33)=0\operatorname{tr}(SA) = \operatorname{tr} \left(\begin{smallmatrix}-3 & 1\\ -3 & 3\end{smallmatrix}\right) = 0: the two parts are “orthogonal” for the trace pairing — an instance of the general fact (proved in the weekend problem of this chapter) that antisymmetric matrices form exactly the annihilator of the symmetric ones. Duality sees the decomposition Mn=Sn⊕An\mathcal{M}_n = \mathcal{S}_n \oplus \mathcal{A}_n before any inner product is chosen.

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