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텐서곱의 행렬표현 📂선형대수

텐서곱의 행렬표현

선형대수
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빌드업1

유한차원 벡터공간 V,VV, V^{\prime}에 대해서 각각 기저 V,V\mathcal{V}, {\mathcal{V}}^{\prime}를 선택하자. 그러면 선형변환 ϕ:VV\phi : V \to V^{\prime}과 동치인 행렬 [ϕ]VV\begin{bmatrix} \phi \end{bmatrix}_{\mathcal{V}}^{{\mathcal{V}}^{\prime}}이 존재하고, 이를 ϕ\phi행렬표현이라 한다. 이제 유한차원 벡터공간 V,V,W,WV, V^{\prime}, W, W^{\prime}와 이들의 순서기저 V,V,W,W\mathcal{V}, {\mathcal{V}}^{\prime}, \mathcal{W}, {\mathcal{W}}^{\prime}, 그리고 두 선형변환 ϕ:VV\phi : V \to V^{\prime}, ψ:WW\psi : W \to W^{\prime}가 주어졌다고 하자.

n=dimV,m=dimV,p=dimW,q=dimW n = \dim V,\quad m = \dim V^{\prime},\quad p = \dim W,\quad q = \dim W^{\prime}

V={vi}i=1n,V={vj}j=1m,W={wk}k=1p,W={wl}l=1q \mathcal{V} = \left\{ v_{i} \right\}_{i=1}^{n},\quad {\mathcal{V}}^{\prime} = \left\{ v_{j}^{\prime} \right\}_{j=1}^{m},\quad \mathcal{W} = \left\{ w_{k} \right\}_{k=1}^{p},\quad {\mathcal{W}}^{\prime} = \left\{ w_{l}^{\prime} \right\}_{l=1}^{q}

그러면 두 선형변환 ϕ\phi, ψ\psi의 행렬표현이 다음과 같이 존재한다.

A=[ϕ]VVMm×nB=[ψ]WWMq×p A = \begin{bmatrix} \phi \end{bmatrix}_{\mathcal{V}}^{{\mathcal{V}}^{\prime}}\in M_{m \times n} \qquad B = \begin{bmatrix} \psi \end{bmatrix}_{\mathcal{W}}^{{\mathcal{W}}^{\prime}} \in M_{q \times p}

텐서곱 VWV \otimes W순서기저VW={viwk}\mathcal{V} \otimes \mathcal{W} = \left\{ v_{i} \otimes w_{k} \right\}과 같이 표기하고, 순서를 다음과 같이 주자.

v1w1,,v1×wp,v2w1,,v2×wp,vnw1,,vn×wp v_{1} \otimes w_{1}, \dots, v_{1} \times w_{p}, \\ v_{2}\otimes w_{1}, \dots, v_{2} \times w_{p}, \\ \dots \\ v_{n}\otimes w_{1}, \dots, v_{n} \times w_{p}

VWV^{\prime} \otimes W^{\prime}의 기저 VW={vjwl}\mathcal{V}^{\prime} \otimes \mathcal{W}^{\prime} = \left\{ v_{j}^{\prime} \otimes w_{l}^{\prime} \right\}에도 같은 식으로 순서를 주자. 그러면 ϕ\phiψ\psi텐서곱 또한 ϕψ:VWVW\phi \otimes \psi : V \otimes W \to V^{\prime} \otimes W^{\prime}인 선형변환이므로 다음과 같은 행렬표현matrix representation이 존재한다.

[ϕψ]VWVW \begin{bmatrix} \phi \otimes \psi \end{bmatrix}_{\mathcal{V} \otimes \mathcal{W}}^{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}}

정리

두 선형변환 ϕ:VV\phi : V \to V^{\prime}, ψ:WW\psi : W \to W^{\prime}의 행렬표현을 각각 A=[ϕ]VVA = \begin{bmatrix} \phi \end{bmatrix}_{\mathcal{V}}^{{\mathcal{V}}^{\prime}}, B=[ψ]WWB = \begin{bmatrix} \psi \end{bmatrix}_{\mathcal{W}}^{{\mathcal{W}}^{\prime}}라 하자. 텐서곱 ϕψ:VWVW\phi \otimes \psi : V \otimes W \to V^{\prime} \otimes W^{\prime}의 행렬표현은 AABB크로네커 곱과 같다.

[ϕψ]VWVW=AB=[ϕ]VV[ψ]WW \begin{bmatrix} \phi \otimes \psi \end{bmatrix}_{\mathcal{V} \otimes \mathcal{W}}^{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}} = A \otimes B = \begin{bmatrix} \phi \end{bmatrix}_{\mathcal{V}}^{{\mathcal{V}}^{\prime}} \otimes \begin{bmatrix} \psi \end{bmatrix}_{\mathcal{W}}^{{\mathcal{W}}^{\prime}}

증명

행렬표현 [ϕψ]VWVW\begin{bmatrix} \phi \otimes \psi \end{bmatrix}_{\mathcal{V} \otimes \mathcal{W}}^{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}}를 찾기 위해서는 정의역의 기저 VW\mathcal{V} \otimes \mathcal{W}ϕψ\phi \otimes \psi에 의해 어떻게 매핑되는지를 보면 된다. 우선 두 선형변환의 행렬표현을 다음과 같다고 하자.

[ϕ]VV=A=[αji]Mm×n[ψ]WW=B=[βlk]Mq×p \begin{bmatrix} \phi \end{bmatrix}_{\mathcal{V}}^{{\mathcal{V}}^{\prime}} = A = [ \alpha_{ji} ] \in M_{m \times n} \qquad \begin{bmatrix} \psi \end{bmatrix}_{\mathcal{W}}^{{\mathcal{W}}^{\prime}} = B = [ \beta_{lk} ] \in M_{q \times p}

다시말해 ϕ(vi)=jαjivj\phi (v_{i}) = \sum\limits_{j}\alpha_{ji}v_{j}^{\prime}, ψ(wk)=lβlkwk\psi (w_{k}) = \sum\limits_{l}\beta_{lk}w_{k}^{\prime}이다. 선형변환의 텐서곱곱 벡터의 정의에 의해 기저벡터 viwkv_{i} \otimes w_{k}는 다음과 같이 매핑된다.

(ϕψ)(viwk)=ϕ(vi)ψ(wk)=(jαjivj)(lβlkwl)=j,lαjiβlkvjwl \begin{align*} (\phi \otimes \psi)(v_{i} \otimes w_{k}) &= \phi (v_{i}) \otimes \psi (w_{k}) \\ &= \left( \sum\limits_{j}\alpha_{ji}v_{j}^{\prime} \right) \otimes \left( \sum\limits_{l}\beta_{lk}w_{l}^{\prime} \right) \\ &= \sum_{j,l} \alpha_{ji}\beta_{lk} v_{j}^{\prime} \otimes w_{l}^{\prime} \\ \end{align*}

    [(ϕψ)(viwk)]VW=[α1iβ1kα1iβ2kα1iβqkα2iβ1kα2iβ2kα2iβqkαmiβ1kαmiβ2kαmiβqk] \implies \left[ (\phi \otimes \psi)(v_{i} \otimes w_{k}) \right]_{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}} = \begin{bmatrix} \alpha_{1i}\beta_{1k} \\ \alpha_{1i}\beta_{2k} \\ \vdots \\ \alpha_{1i}\beta_{qk} \\ \alpha_{2i}\beta_{1k} \\ \alpha_{2i}\beta_{2k} \\ \vdots \\ \alpha_{2i}\beta_{qk} \\ \vdots \\ \alpha_{mi}\beta_{1k} \\ \alpha_{mi}\beta_{2k} \\ \vdots \\ \alpha_{mi}\beta_{qk} \\ \end{bmatrix}

따라서 정리하면 다음과 같다.

[ϕψ]VWVW=[[ϕ(v1)ψ(w1)]VW[ϕ(v1)ψ(w2)]VW[ϕ(vn)ψ(wp)]VW]=[α11β11α11β12α11β1pα1nβ11α1nβ12α1nβ1pα11β21α11β22α11β2pα1nβ21α1nβ22α1nβ2pα11βq1α11βq2α11βqpα1nβq1α1nβq2α1nβqpα21β11α21β12α21β1pα2nβ11α2nβ12α2nβ1pα21β21α21β22α21β2pα2nβ21α2nβ22α2nβ2pα21βq1α21βq2α21βqpα2nβq1α2nβq2α2nβqpαm1β11αm1β12αm1β1pαmnβ11αmnβ12αmnβ1pαm1β21αm1β22αm1β2pαmnβ21αmnβ22αmnβ2pαm1βq1αm1βq2αm1βqpαmnβq1αmnβq2αmnβqp]=[α11[β11β12β1pβ21β22β2pβq1βq2βqp]α1n[β11β12β1pβ21β22β2pβq1βq2βqp]α21[β11β12β1pβ21β22β2pβq1βq2βqp]α2n[β11β12β1pβ21β22β2pβq1βq2βqp]αm1[β11β12β1pβ21β22β2pβq1βq2βqp]αmn[β11β12β1pβ21β22β2pβq1βq2βqp]]=[α11Bα1nBα21Bα2nBαm1BαmnB]=AB \begin{align*} & \begin{bmatrix} \phi \otimes \psi \end{bmatrix}_{\mathcal{V} \otimes \mathcal{W}}^{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}} \\ &= \begin{bmatrix} \left[ \phi (v_{1}) \otimes \psi (w_{1}) \right]_{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}} & \left[ \phi (v_{1}) \otimes \psi (w_{2}) \right]_{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}} & \cdots & \left[ \phi (v_{n}) \otimes \psi (w_{p}) \right]_{{\mathcal{V}}^{\prime} \otimes \mathcal{W}^{\prime}} \end{bmatrix} \\ &= \left[ \begin{array}{cccc|c|cccc} \alpha_{11}\beta_{11} & \alpha_{11}\beta_{12} & \cdots & \alpha_{11}\beta_{1p} & \cdots & \alpha_{1n}\beta_{11} & \alpha_{1n}\beta_{12} & \cdots & \alpha_{1n}\beta_{1p} & \\ \alpha_{11}\beta_{21} & \alpha_{11}\beta_{22} & \cdots & \alpha_{11}\beta_{2p} & \cdots & \alpha_{1n}\beta_{21} & \alpha_{1n}\beta_{22} & \cdots & \alpha_{1n}\beta_{2p} & \\ \vdots & \vdots & \ddots & \vdots & \cdots & \vdots & \vdots & \ddots & \vdots \\ \alpha_{11}\beta_{q1} & \alpha_{11}\beta_{q2} & \cdots & \alpha_{11}\beta_{qp} & \cdots & \alpha_{1n}\beta_{q1} & \alpha_{1n}\beta_{q2} & \cdots & \alpha_{1n}\beta_{qp} & \\ \hline \alpha_{21}\beta_{11} & \alpha_{21}\beta_{12} & \cdots & \alpha_{21}\beta_{1p} & \cdots & \alpha_{2n}\beta_{11} & \alpha_{2n}\beta_{12} & \cdots & \alpha_{2n}\beta_{1p} & \\ \alpha_{21}\beta_{21} & \alpha_{21}\beta_{22} & \cdots & \alpha_{21}\beta_{2p} & \cdots & \alpha_{2n}\beta_{21} & \alpha_{2n}\beta_{22} & \cdots & \alpha_{2n}\beta_{2p} & \\ \vdots & \vdots & \ddots & \vdots & \cdots & \vdots & \vdots & \ddots & \vdots \\ \alpha_{21}\beta_{q1} & \alpha_{21}\beta_{q2} & \cdots & \alpha_{21}\beta_{qp} & \cdots & \alpha_{2n}\beta_{q1} & \alpha_{2n}\beta_{q2} & \cdots & \alpha_{2n}\beta_{qp} & \\ \hline \vdots & \vdots & \vdots & \vdots & \ddots & \vdots & \vdots & \vdots & \vdots \\ \hline \alpha_{m1}\beta_{11} & \alpha_{m1}\beta_{12} & \cdots & \alpha_{m1}\beta_{1p} & \cdots & \alpha_{mn}\beta_{11} & \alpha_{mn}\beta_{12} & \cdots & \alpha_{mn}\beta_{1p} & \\ \alpha_{m1}\beta_{21} & \alpha_{m1}\beta_{22} & \cdots & \alpha_{m1}\beta_{2p} & \cdots & \alpha_{mn}\beta_{21} & \alpha_{mn}\beta_{22} & \cdots & \alpha_{mn}\beta_{2p} & \\ \vdots & \vdots & \ddots & \vdots & \cdots & \vdots & \vdots & \ddots & \vdots \\ \alpha_{m1}\beta_{q1} & \alpha_{m1}\beta_{q2} & \cdots & \alpha_{m1}\beta_{qp} & \cdots & \alpha_{mn}\beta_{q1} & \alpha_{mn}\beta_{q2} & \cdots & \alpha_{mn}\beta_{qp} & \\ \end{array} \right] \\ &= \left[ \begin{array}{c} \alpha_{11} \begin{bmatrix} \beta_{11} & \beta_{12} & \cdots & \beta_{1p} \\ \beta_{21} & \beta_{22} & \cdots & \beta_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ \beta_{q1} & \beta_{q2} & \cdots & \beta_{qp} \end{bmatrix} & \cdots & \alpha_{1n}\begin{bmatrix} \beta_{11} & \beta_{12} & \cdots & \beta_{1p} \\ \beta_{21} & \beta_{22} & \cdots & \beta_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ \beta_{q1} & \beta_{q2} & \cdots & \beta_{qp} \end{bmatrix} \\ \alpha_{21} \begin{bmatrix} \beta_{11} & \beta_{12} & \cdots & \beta_{1p} \\ \beta_{21} & \beta_{22} & \cdots & \beta_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ \beta_{q1} & \beta_{q2} & \cdots & \beta_{qp} \end{bmatrix} & \cdots & \alpha_{2n}\begin{bmatrix} \beta_{11} & \beta_{12} & \cdots & \beta_{1p} \\ \beta_{21} & \beta_{22} & \cdots & \beta_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ \beta_{q1} & \beta_{q2} & \cdots & \beta_{qp} \end{bmatrix} \\ \vdots & \ddots & \vdots \\ \alpha_{m1} \begin{bmatrix} \beta_{11} & \beta_{12} & \cdots & \beta_{1p} \\ \beta_{21} & \beta_{22} & \cdots & \beta_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ \beta_{q1} & \beta_{q2} & \cdots & \beta_{qp} \end{bmatrix} & \cdots & \alpha_{mn}\begin{bmatrix} \beta_{11} & \beta_{12} & \cdots & \beta_{1p} \\ \beta_{21} & \beta_{22} & \cdots & \beta_{2p} \\ \vdots & \vdots & \ddots & \vdots \\ \beta_{q1} & \beta_{q2} & \cdots & \beta_{qp} \end{bmatrix} \\ \end{array} \right] \\ &= \begin{bmatrix} \alpha_{11} B & \cdots & \alpha_{1n} B \\ \alpha_{21} B & \cdots & \alpha_{2n} B \\ \vdots & \ddots & \vdots \\ \alpha_{m1} B & \cdots & \alpha_{mn} B \end{bmatrix} \\ &= A \otimes B \end{align*}


  1. 김영훈·허재성, 양자 정보 이론 (2020), p36 ↩︎