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분류 전체보기 272

[선형대수학] 선형 사상을 모은 집합과 행렬을 모은 집합 간에는 선형 사상이 존재한다. (Existence of Linear Map Between Set of Linear Maps and Set of Matrices)

$\underline{Thm}$ Let $V,W$ be finite dimensional vector space over $\mathbb{F}$ with ordered basis $\beta,\gamma$, respectively. Let $T,U \colon V \rightarrow W$ be linear & $a \in \mathbb{F}$. Then $$[T+U]_{\beta}^{\gamma}=[T]_{\beta}^{\gamma}+[U]_{\beta}^{\gamma},$$ $$[aT]_{\beta}^{\gamma}=a[T]_{\beta}^{\gamma}.$$ That is, the map $\mathcal{J} \colon \mathcal{L}(V,W) \rightarrow M_{m \times n..

[선형대수학] 벡터 공간 간의 사상을 모은 집합은 벡터 공간이 된다.

$\underline{Def}$ Let $V,W$ be vector space over $\mathbb{F}$ and let $a \in \mathbb{F}$. Let $T,U \colon V \rightarrow W$ be any maps. We define two maps $T+U,aT \colon V \rightarrow W$ by $$(T+U)(v)=T(v)+U(v)$$ $$(aT)(v)=aT(v)\,for\, v \in V$$ $\underline{Rmk}$ Let $\mathcal{F}(V,W)$ be the set of all maps from $V$ into $W$. With the operations of additaion & scalar multiplication above, $\mat..

[선형대수학] 선형 변환의 행렬 표현 (Matrix Representation of a Linear Transform)

$\underline{Def}$ Let $V,W$ be finite-dimensional vector spaces with ordered basis $\beta=\{v_{1},\cdots,v_{n}\},\,\gamma=\{w_{1},\cdots,w_{m}\}$, respectively. Let $T \colon V \rightarrow W$ be linear. For each $v_{j}(1\leq j\leq n)$, since $T(v_{j}) \in W$, $\exists ! a_{1j},\cdots,a_{mj} \in \mathbb{F}$ such that $\displaystyle T(v_{j})=\sum_{i=1}^{m} a_{ij}w_{i}$. We write $[T]_{\beta}^{\gam..

[선형대수학] 선형 변환이 일대일인 것과 동치 명제들

$\underline{Thm}$ Let $V,W$ be finite-dimensional vector space of equal dimension. Let $T \colon V \rightarrow W$ be linear. Then TFAE: $(a)$ $T$ is 1-1. $(b)$ $T$ is onto. $(c)$ $rank(T)=dim(V)$ $\underline{Proof}$ Write $n=dim(V)=dim(W)$. $(a) \Rightarrow (b)$ Since $T$ is 1-1, by thm, $nullity(T)=0$. By Dimension Theorem, $n=dim(V)=nullity(T)+rank(T)=0+rank(T)$,and so $dim(R(T))=n$. Thus, $R(..

[선형대수학] 영공간, 치역, 널리티, 랭크 (Null space, Range, Nullity, Rank)

$\underline{Def}$ Let $V,W$ be vector spaces, and let $T \colon V \rightarrow W$ be linear. We define the null space $N(T)$, the range $R(T)$ of $T$. $$ N(T)=\{ x \in V \colon Tx=0 \} $$ $$ R(T)=\{ Tx \in W \colon x \in V \} $$ Null space and range are called kernel and image respectively. $\underline{Thm}$ Let $V, W$ & $T$ be an in the previous definition. Then $N(T), R(T)$ are subspaces of $V,..

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