Linear Algebra & Applications

2201NSC

Vectors Spaces

Definition

Let $V$ be a nonempty set on which are defined operations "$+$" (called addition) and "$\cdot$" (called scalar multiplication).

$V$ is a vector space (over $\BF$) if the following hold for all $\bfx,\bfy,\bfz\in V$ and all $\alpha,\beta\in\BF$:

(V1) $\bfx+\bfy\in V$ (closure)
(V2) $\bfx+\bfy=\bfy+\bfx$ (additive commutativity)
(V3) $\bfx+(\bfy+\bfz)=(\bfx+\bfy)+\bfz$ (additive associativity)


Definition

$V$ is a vector space (over $\BF$) if the following hold for all $\bfx,\bfy,\bfz\in V$ and all $\alpha,\beta\in\BF$:

(V1) $\bfx+\bfv\in V$ (closure)
(V2) $\bfx+\bfy=\bfy+\bfx$ (additive commutativity)
(V3) $\bfx+(\bfy+\bfz)=(\bfx+\bfy)+\bfz$ (additive associativity)
(V4) $\exists\,{\bf 0}\in V$ such that $\bfx+{\bf 0}=\bfx$ (zero vector, or additive identity)
(V5) For each $\bfx\in V$, $\exists\,(-\bfx)\in V$ such that $\bfx+(-\bfx)={\bf 0}$ (additive inverse)
(V6) $\alpha \bfx\in V$ (closure)
(V7) $\alpha(\bfx+\bfy)=\alpha\bfx+\alpha\bfy$ (multiplicative-additive distributivity)
(V8) $\left(\alpha + \beta \right)\bfx=\alpha\bfx+\beta\bfx$ (additive-multiplicative distributivity)
(V9) $\alpha(\beta\bfx)=(\alpha\beta)\bfx$ (multiplicative-multiplicative distributivity)
(V10) $1\bfx=\bfx$ (multiplicative identity)

Example 1: $\BF^n$ - set of $n$-tuples

1) Identify elements of the set: $$\BF^n = \big\{ \u = \left( u_1, u_2, \ldots , u_n\right)~|~ u_1, u_2, \ldots , u_n \in \BF \big\}$$

2) & 3) Check for closure of addition and scalar multiplication: $$\u+ \v = \left( u_1+v_1, u_2+v_2, \ldots , u_n+v_n\right)$$ $$\;\,k \cdot \u = \left( ku_1, ku_2, \ldots , ku_n\right)$$

4) Identify the vector zero: $\mathbf 0 = \left( 0, 0, \ldots , 0\right)$

5) Identify the inverse additive: $ - \mathbf u = \left( -u_1, -u_2, \ldots , -u_n\right)$



Example 2: $\BF^n$ - set of $n$-tuples

1) Identify elements of the set: $$\BF^n = \big\{ \u = \left( u_1, u_2, \ldots , u_n\right)~|~ u_1, u_2, \ldots , u_n \in \BF \big\}$$

2) & 3) Check for closure of addition and scalar multiplication: $$\u+ \v = \left( u_1+v_1, u_2+v_2, \ldots , u_n+v_n\right)$$ $$\;\,k \cdot \u = \left( ku_1, ku_2, \ldots , ku_n\right)$$

4) Identify the vector zero: $\mathbf 0 = \left( 0, 0, \ldots , 0\right)$

5) Identify the inverse additive: $ - \mathbf u = \left( -u_1, -u_2, \ldots , -u_n\right)$

Now you can continue checking that the other properties hold. 📝

Remark: This is just an strategy you can use. But if you prefer, you can verify each property one by one in the given order, that is, from (V1) to (V10).


Example 3: $M_{m,n}(\BF)$ - set of $m\times n$ matrices

1) $M_{m,n} = \left\{ \left( \begin{array}{ccc} a_{11} & \cdots & a_{1n} \\ \vdots & \ddots & \vdots \\ a_{m1} & \cdots & a_{mn} \\ \end{array} \right) ~\Bigg|~ a_{ij}\in \BF, 1\leq i\leq m, 1\leq j\leq n \right\}$

2) & 3) Usual addition and scalar multiplication for matrices.

4) $\mathbf 0 = \left( \begin{array}{ccc} 0 & \cdots & 0 \\ \vdots & \ddots & \vdots \\ 0 & \cdots & 0 \\ \end{array} \right)$

5) $- \mathbf u = \left( \begin{array}{ccc} -a_{11} & \cdots & -a_{1n} \\ \vdots & \ddots & \vdots \\ -a_{m1} & \cdots & -a_{mn} \\ \end{array} \right)$


Example 4: $C[a,b]$ - set of continuous real-valued functions on $[a,b]$

1) $\mathbf f, \mathbf g\in C[a,b],$ often represented as $f(x), g(x).$

2) & 3) $\left(\,f+g\right)(x) = f(x) + g(x)$
$\qquad \quad \,\, (k \cdot f)(x) = kf(x)$

4) $\mathbf 0 = \,?$ 🤔

5) $ - \mathbf f = -f(x) $




Example 5: $P_n(\BF)$ - set of polynomials of degree at most $n$

1) $\mathbf p \in P_n(\BF)$, with $\mathbf p = a_0 + a_1x + \cdots + a_n x^n$ and $a_k\in \BF,$ $\forall k.$

2) & 3) Operations similar to Example 4.

4) $\mathbf 0 = \,?$ 🤔

5) $ - \mathbf p = -p(x) .$





Example 6: Set of solutions to a homogeneous linear ODE

For example: $y''+p(x) y' + q(x) y = 0 $ ($y=y(x)$).

1) Let $y_1$ and $y_2$ be solutions.

2) Operations similar to Example 4.

3) Superposition principle: $y_1+y_2 $ is also a solution.

4) The zero vector is the zero function.

5) Given a solution $y$, $-y$ is also a solution.




Vector space representation












Vector space representation






















Vector space representation











Vector space representation

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