Difference between space, subspace, subset, span, and ambient space

1. Overview

2. Description

2.1 Space

2.2 Subspace

• Be closed under addition and scalar multiplication
• Contain the zero vector

$\forall \boldsymbol{v},\boldsymbol{w}\in V$; $\forall \lambda ,\alpha \in \mathbb{R}$; $\lambda\boldsymbol{v} +\alpha\boldsymbol{w} \in V$

2.3 Subset

2.3.1 Definition

2.3.2 How to distinguish subset and subspace

Step 1: Determine whether the origin is in the set

Step 2: Try to write down the criteria in terms of scalars and vectors of the form $\alpha v+\beta w$

2.4 span

2.4.1 Definition

Given a vector space V over a field K, the span of a set S of vectors (not necessarily infinite) is defined to be the intersection W of all subspaces of V that contain S. W is referred to as the subspace spanned by S, or by the vectors in S. Conversely, S is called a spanning set of W, and we say that S spans W.

Alternatively, the span of S may be defined as the set of all finite linear combinations of elements (vectors) of S, which follows from the above definition.

$$span({\boldsymbol{v}_{1},\cdots ,\boldsymbol{v}_{n}})=\alpha _{1}\boldsymbol{v}_{1}+\cdots +\alpha _{n}\boldsymbol{v}_{n},\: \alpha \in \mathbb{R}$$

2.4.2 theorems

Theorem 1: The subspace spanned by a non-empty subset S of a vector space V is the set of all linear combinations of vectors in S.

This theorem is so well known that at times it is referred to as the definition of span of a set.

Theorem 2: Every spanning set S of a vector space V must contain at least as many elements as any linearly indenpendent set of vectors from V.

Theorem 3: Let V be a finite-dimensional vector space. Any set of vectors that spans V can be reduced to a basis for V by discarding vectors if necessary (i.e. if there are linearly dependent vectors in the set). If the axiom of choice holds, this is true without the assumption that V has finite dimension.

This also indicates that a basis is a minimal spanning set when V is finite-dimensional.

3. Example

3.1 Subset

3.2 Subset 2

3.3 Subset and subspace

3.4 Subset

3.5 More vectors $\neq$ more dimensions

3.6 Span

3.6.1 Span $\mathbb{R}^{2}$

Let $S_{1}=\begin{Bmatrix} \begin{pmatrix} 1\\  1\\  0 \end{pmatrix},\begin{pmatrix} 1\\  7\\  0 \end{pmatrix} &  \end{Bmatrix}$, $S_{2}=\begin{Bmatrix} \begin{pmatrix} 1\\  1\\  0 \end{pmatrix}, & \begin{pmatrix} 1\\  7\\  0 \end{pmatrix} & ,\begin{pmatrix} -1\\  2\\  1 \end{pmatrix} \end{Bmatrix}$

$$span(S_{1})=span(S_{2})=\mathbb{R}^{2}$$

4. References

https://en.wikipedia.org/wiki/Subset

https://en.wikipedia.org/wiki/Space_(mathematics)

https://en.wikipedia.org/wiki/Linear_subspace

http://sincxpress.com/

https://en.wikipedia.org/wiki/Ambient_space

https://en.wikipedia.org/wiki/Linear_span