5: Vector Spaces
- Page ID
- 1727
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)The two key properties of vectors are that they can be added together and multiplied by scalars, so we make the following definition.
Definition
A \(\textit{vector space}\) \((V,+,.\, ,\mathbb{R})\) is a set \(V\) with two operations \(+\) and \(\cdot\) satisfying the following properties for all \(u, v \in V\) and \(c, d \in \mathbb{R}\):
- (Additive Closure) \(u+v \in V\). \(\textit{Adding two vectors gives a vector.}\)
- (Additive Commutativity) \(u+v=v+u\). \(\textit{Order of addition doesn't matter.}\)
- (Additive Associativity) \((u+v)+w = u+(v+w)\). \(\textit{Order of adding many vectors doesn't matter.}\)
- (Zero) There is a special vector \(0_V \in V\) such that \(u+0_V = u\) for all \(u\) in \(V\).
- (Additive Inverse) For every \(u \in V\) there exists \(w \in V\) such that \(u+w=0_V\).
- (Multiplicative Closure) \(c\cdot v \in V\). \(\textit{Scalar times a vector is a vector.}\)
- (Distributivity) \((c+d) \cdot v= c\cdot v + d\cdot v\). \(\textit{Scalar multiplication distributes over addition of scalars.}\)
- (Distributivity) \(c\cdot (u+v)= c\cdot u + c\cdot v\). \(\textit{Scalar multiplication distributes over addition of vectors.}\)
- (Associativity) \((cd)\cdot v = c \cdot (d \cdot v)\).
- (Unity) \(1\cdot v = v\) for all \(v \in V\).
Remark
Rather than writing \((V,+,.\, ,\mathbb{R})\), we will often say "let \(V\) be a vector space over \(\mathbb{R}\)''. If it is obvious that the numbers used are real numbers, then "let \(V\) be a vector space'' suffices. Also, don't confuse the scalar product with the dot product. The scalar product is a function that takes as inputs a number and a vector and returns a vector as its output. This can be written:
\[\cdot \colon \mathbb{R}\times V \rightarrow V\, .\]
Similarly
\[ +:V\times V \rightarrow V\, . \]
On the other hand, the dot product takes two vectors and returns a number. Succinctly: \(\cdot \colon V\times V \rightarrow \Re\). Once the properties of a vector space have been verified, we'll just write scalar multiplication with juxtaposition \(cv=c\cdot v\), though, to avoid confusing the notation.
- 5.1: Examples of Vector Spaces
- One can find many interesting vector spaces, such as the following:
- 5.2: Other Fields
- Above, we defined vector spaces over the real numbers. One can actually define vector spaces over any field. This is referred to as choosing a different base field. A field is a collection of "numbers'' satisfying certain properties.
Contributor
David Cherney, Tom Denton, and Andrew Waldron (UC Davis)