이 포스트에서 $V, W$는 모두 $F$-벡터공간으로 취급한다.Theorem 1 (The Fundamental Theorem of Linear Algebra)Theorem 1. Let dim($V$) = $n$ and dim($W$) = $m$, and let $\beta, \gamma$ be ordered bases for $V, W$, respectively. Then the function $\Phi: \mathcal{L}(V, W) \rightarrow M_{m \times n}(F)$, defined by $\Phi(T) = [T]_{\beta}^{\gamma}$ for $\forall T \in \mathcal{L}(V, W)$, is an isomorphism.Proof. (1) $\Phi$..

이 포스트에서 $V, W$는 모두 $F$-벡터공간으로 취급한다.Inverse of a matrixDefinition 1. Let $A \in M_{n \times n}(F)$. Then $A$ is invertible if $\exists B \in M_{n \times n}(F)$ such that $AB = BA = I_n$. The matrix $B$ is called the inverse of $A$ and is denoted by $A^{-1}$.IsomorphismDefinition 2. We say that $V$ and $W$ are isomorphic, denoted $V \cong W$, if $\exists T \in \mathcal{L}(V, W)$ such that $T$ is i..

Left-multiplication transformationDefinition 1. Let $A \in M_{m \times n}(F)$. We denote by $L_A$ the mapping $L_A: F^n \longrightarrow F^m$ defined by $\mathsf{L}_A(x) = Ax, \forall x \in F^n$. We call $\mathsf{L}_A$ a left-multiplication transformation.Theorem 1Theorem 1. Let $A, B \in M_{m \times n}(F)$. Then we have the following properties: (a) Every left-multiplication is linear.(b) $L_A \..

Kronecker delta Definition 1. We define the Kronecker delta $\delta_{ij}$ by $\delta_{ij} = \begin{cases} 1 & \text{if } i = j \\ 0 & \text{if } i \neq j. \end{cases}$ Identity matrix Definition 2. The $n \times n$ identity matrix $I_n$ is defined by $(I_n)_{ij} = \delta_{ij}$. Remark Remark. Let $A \in M_{n \times n}(F).$ Then $A$ is a diagonal matrix $\Longleftrightarrow A_{ij} = \delta_{ij} A..

이 포스트에서 $V, W, Z$는 모두 유한차원 $F$-벡터공간으로 취급한다. 함수의 합성은 보통 $g \circ f$로 표기하는데, linear transformation의 경우 $gf$로 표기하도록 하자.Theorem 1Theorem 1. Let $T, U_1, U_2 \in \mathcal{L}(V, W)$, and let $U \in \mathcal{L}(W, Z)$. Then (a) $UT \in \mathcal{L}(V, Z)$.(b) If $UT$ is injective, then so is $T$.(c) If $UT$ is surjective, then so is $U$.(d) IF $T$ and $U$ are bijective, then so is $UT$. Introduction    ..

이 포스트에서 $V, W$는 모두 유한차원 $F$-벡터공간으로 취급한다.Ordered basisDefinition 1. An ordered basis for $V$ is a basis for $V$ endowed with a specific order.    기저에 순서를 부여한 것을 ordered basis, 순서기저라고 부른다. 즉 순서기저로 생각하면 $\{e_1, e_2, e_3\} \neq \{e_2, e_1, e_3\}$이다. Coordinate vectorDefinition 2. Let $\beta = \{v_1, ..., v_n\}$ be an ordered basis for V. We define the coordinate vector of $x$ relative to $\beta$, de..

이 포스트에서 $V, W$는 모두 $F$-벡터공간으로 취급한다.The nullity and rankDefinition 2. Let $T \in \mathcal{L}(V, W)$. If $N(T)$ and $R(T)$ are finite-dimensional, then we define (1) the nullity of $T$, denoted nullity($T$) := dim($N(T)$), (2) the rank of $T$, denoted rank($T$) := dim($R(T)$).    $N(I_V) = \{\mathbf{0}\}, R(I_V) = V$, 그리고 $N(T_0) = V, R(T_0) = \{\mathbf{0}\}$ 임을 생각해 볼 때, 직관적으로 nullity가 클수록 rank는 작..

이 포스트에서 $V, W$는 모두 $F$-벡터공간으로 취급한다.The null space and rangeDefinition 1. Let $T \in \mathcal{L}(V, W)$.(a) The null space (or kernel) $N(T)$ of $T$ is the set $N(T) = \{ x \in V \,|\, T(x) = \mathbf{0} \}.$(b) The range (or image) $R(T)$ of $T$ is the set $R(T) = \{ T(x) \in W \,|\, x \in V \}$. Theorem 1Theorem 1. Let $T \in \mathcal{L}(V, W)$. Then $N(T) \leq V$ and $R(T) \leq W$.Proof. Clearl..

이 포스트에서 $V, W$는 모두 $F$-벡터공간으로 취급한다.Linear TransformationDefinition 1. We call a function $T : V \rightarrow W$ a linear transformation from $V$ to $W$ if, $\forall x, y \in V$ and $c \in F$, we have (a) $T(x + y) = T(x) + T(y)$ and(b) $T(cx) = cT(x)$.    어떤 함수가 linear transformation이라는 것을 줄여서 linear라고 말하기도 한다.Linear OperaterDefinition 2. We call $T$ a linear operator on $V$ if $T$ is a linear tr..

Definition Definition. Let $x_1, x_2 \in$ an interval $I$ such that $x_1 f(x_2). $$ Theorem Theorem. Let $f: I \longrightarrow \mathbb{R}$ be a differentiable function on $I$. Then for $\forall x \in I$, $$f'(x) > 0 \Longrightarrow f \text{ is incre..