“His words span rivers and mountains, but his thoughts are still only six inches long.”
―E.B. White
Table13.36.Modules vs. Vector Spaces
Module
Vector Space
Arbitrary \(R\)-module \(M\)
Arbitrary \(F\)-Vector Space \(V\)
\(R\)-module homomorphism
\(F\)-linear transformation
Submodule Generated by \(A\)
\(\Span(A)\)
Rank
Dimension
Definition13.37.Span.
In keeping with standard convention, we use the term “span” instead of “submodule generated by”, but they mean exactly the same thing: for a subset \(A\) of an \(F\)-vector space \(V\text{,}\) the span of \(A\) is
When \(\Span(A)=F\text{,}\) we say that \(A\)spans\(F\text{.}\)
As with modules over general rings, the span of a subset of a vector space is a sub-vector space (i.e., sub-module).
Lemma13.38.Linear Independence and Span.
Suppose \(I\) is a linearly independent subset of an \(F\)-vector space \(V\) and \(v \in V \setminus \Span(I)\text{,}\) then \(I \cup \{v\}\) is also linearly independent. 1
Stated in terms of modules: Let \(R\) be a field. Suppose \(A\) is a linearly independent subset of an \(R\)-module \(M\) and \(m \in M \setminus RA\text{,}\) then \(A \cup \{m\}\) is also linearly independent.
Proof.
Let \(w_1, \dots, w_n\) be a list of distinct elements of \(I \cup \{v\}\) and suppose
for some \(c_i \in F\text{.}\) If \(v \ne w_i\) for all \(i\text{,}\) then \(c_i = 0\) for all \(i\text{,}\) since \(I\) is linearly independent. Thus \(v=w_i\) for some \(i\leq n\text{.}\) Without loss of generality, say \(w_1 = v\text{.}\) If \(c_1 \ne 0\text{,}\) then we may write
But then \(v \ne w_i\) for all \(i\text{,}\) and thus we have \(c_i = 0\) for all \(i \geq 2\) by the same reasoning as in the first case (since \(I\) is linearly independent). Thus \(A \cup \{m\}\) is linearly independent.
Remark13.39.
The only place where the fact that the ring of scalars is a field is to know that \(c_1\) has a multiplicative inverse when \(c_1 \ne 0\text{.}\) In particular, Lemma 13.38 holds when \(F\) is a division ring too.
To prove that every vector space has a basis, we will need to use Zorn’s Lemma.
Theorem13.40.Finding Intermediate Bases.
Let \(V\) be an \(F\)-vector space and assume \(I \subseteq S \subseteq V\) are subsets such that \(I\) is linearly independent and \(S\) spans \(V\text{.}\) Then there is a subset \(B\) such that \(I \subseteq B \subseteq S\) and \(B\) is a basis of \(V.\)
Proof.
Let \(\cP\) denote the collection of all subsets \(X\) of \(V\) such that \(I\subseteq X \subseteq S\) and \(X\) is linearly independent. We make \(\cP\) into a poset by the order relation \(\subseteq\text{,}\) set containment. Note that \(I \in \cP\text{.}\)
Let \(\cT\) be any totally ordered subset of \(\cP\text{.}\) If \(\cT\) is empty, then \(\cT\) is (vacuously) bounded above by \(I\text{.}\) Assume \(\cT\) is non-empty. Let \(Z = \bigcup_{Y \in \cT} Y\text{.}\)
Given \(z_1, \dots, z_m \in Z\text{,}\) for each \(i\) we have \(z_i \in Y_i\) for some \(Y_{i} \in \cT\text{.}\) Since \(\cT\) is totally ordered, one of \(Y_1, \dots, Y_m\) contains all the others and hence it contains all the \(z_i\)’s. Since each \(Y_i\) is linearly independent, this shows \(z_1, \dots, z_m\) are linearly independent. We have shown that every finite subset of \(Z\) is linearly independent, and hence \(Z\) is linearly independent. Since \(\cT\) is non-empty, \(Z \supseteq I\text{.}\) Since each member of \(\cT\) is contained in \(S\text{,}\)\(Z \subseteq S\text{.}\) Thus, \(Z \in \cP\text{,}\) and it is an upper bound for \(\cT\text{.}\) We may thus apply Zorn’s Lemma to conclude that \(\cP\) has at least one maximal element, \(B\text{.}\)
Note that \(B\) is linearly independent and \(I \subseteq B \subseteq S\) by construction. Suppose by way of contradiction that \(B\) does not span \(V\text{.}\) Since \(S\) spans \(V\text{,}\) if \(S \subseteq \Span(B)\text{,}\) then \(\Span(B)\) would have to be all of \(V\text{.}\) 2
For note that if \(\Span(S) = V\) and \(S \subseteq \Span(B)\text{,}\) then for any \(v \in V\) we may write \(v = \sum_i c_i s_i\) for \(s_i \in S\) and \(s_i = \sum_j a_{i,j} b_{i,j}\) with \(b_{i,j} \in B\) and hence \(v= \sum_{i,j} c_i a_{i,j} b_{i,j}\text{,}\) which implies \(\Span(B) = V\text{.}\)
Since we are assuming \(\Span(B) \ne V\text{,}\) there must be at least one \(v \in S\) such that \(v \notin \Span(B)\text{.}\)
Consider \(B \cup \{v\}\text{.}\) Then \(I \subset B \cup \{v\} \subseteq S\) and, by Lemma 13.38, \(B \cup \{v\}\) is linearly independent. This shows that \(B \cup \{v\}\) is an element of \(\cP\) that is strictly bigger than \(B\text{,}\) contrary to the maximality of \(B\text{.}\) So, \(B\) must span \(V\) and hence it is a basis.
Corollary13.41.Every Vector Space has a Basis.
Every \(F\)-vector space \(V\) has a basis.
Every linearly independent subset of \(V\) is contained in some basis
Every set of vectors that spans \(V\) contains some basis.
Proof.
Apply the Theorem 13.40 with \(I = \emptyset\) and \(S = V\text{.}\)
Apply the Theorem 13.40 with \(I\) arbitrary and \(S = V\)
Apply the Theorem 13.40 with \(I = \emptyset\) and \(S\) arbitrary.
In particular, this says that every module over a field is free!
Qual Watch.
Proving Item 2 of Corollary 13.41 was [cross-reference to target(s) "june-2010-6" missing or not unique] on the [cross-reference to target(s) "june-2010" missing or not unique] qualifying exam.
Example13.42.
\(\R\) has a basis as a \(\Q\)-vector space. Just don’t ask me what it looks like. 3
This being one of those instances when the destination is more important than the journey
Corollary13.43.Basis of Subspaces Extend.
Suppose \(F\) is a field an \(W\) is a subspace (i.e., submodule) of the \(F\)-vector space (i.e., \(F\)-module) \(V\text{.}\) Then every basis of \(W\) extends to a basis of \(V\) - that is, if \(B\) is a basis of \(W\) then there exists a basis \(\tilde{B}\) of \(V\) such that \(B\) is a subset of \(\tilde{B}\text{.}\)
Proof.
Apply Corollary 13.41 with \(B = I\) and \(S = V\text{.}\) (Since \(B\) is a basis of \(W\text{,}\) it is linearly independent, and observe that \(B\) remains linearly independent when regarded as a subset of \(V\text{.}\))
Remark13.44.
It is not true that, with the notation of Corollary 13.43, if \(\tilde{B}\) is a basis of \(V\) then there exists a basis \(B\) of \(W\) such that \(B\) is a subset of \(\tilde{B}\text{.}\) For instance, take \(F = \R\text{,}\)\(V = \R^2\text{,}\)\(\tilde{B} = \{(1,0), (0,1)\}\) and \(W\) the subspace spanned by \((1,1)\text{.}\)
SubsectionEvery Basis of a Vector Space has the Same Dimension
In the language of modules, a finite dimensional vector space is just a finitely generated \(F\)-module.
The following is an essential property of vector spaces that eventually will allow us to compare bases in terms of size.
Lemma13.45.Exchange Lemma.
Let \(F\) be a field, let \(B\) be a basis of an \(F\)-vector space \(V\text{,}\) and let \(C\) be any finite set of \(m\) linearly independent vectors in \(V\text{.}\) Then there are distinct vectors \(v_1, \dots, v_m\) in \(B\text{,}\) such that \((B \setminus \{v_1, \dots, v_m\}) \cup C\) is also a basis \(V\text{.}\)
Proof.
Let \(C = \{w_1, \dots, w_m\}\text{.}\)Lemma 13.38 proves the case when \(m = 1\text{.}\) The general case proceeds recursively:
Suppose that for some \(0 \leq k < m\text{,}\) we have found \(v_1, \dots, v_k \in B\) such that \(B'' := (B \setminus \{v_1, \dots, v_k\}) \cup \{w_1, \dots, w_k\}\) is a basis for some \(k < m\text{.}\) We need to show we can “swap out one more’’; that is, we need to prove there is a \(v_{k+1} \in(B \setminus \{v_1, \dots, v_k\})\) such that \((B'' \setminus \{v_{k+1}\}) \cup \{w_{k+1}\}\) is also a basis.
with \(u_i \in B''\) and \(a_i \ne 0\text{.}\) Now, there must be at least one \(u_i\) that is not in \(\{w_1, \dots, w_k\}\text{,}\) for otherwise we would have \(w_{k+1} \in \Span( w_1, \dots, w_k)\text{,}\) contrary to \(C\) being linearly independent. Let \(v_{k+1} = u_i\) for such an \(i\text{.}\) Then by Lemma 13.38
The Exchange Lemma also holds for any division ring (using the exact same proof).
Definition13.47.Dimension.
The dimension of a vector space \(V\text{,}\) denoted \(\dim_F(V)\text{,}\) is the cardinality of any of its bases. A vector space is finite dimensional if there is spanned by a finite subset.
Remark13.48.
This is the same as the rank of \(V\) as an \(F\)-module.
But what if two bases of \(V\) have different cardinalities? I’m so glad you asked.
Theorem13.49.Dimension Theorem.
Any two bases of the same vector space have the same dimension.
Proof.
We will only prove this in the case of finite dimensional vector spaces, but it is indeed true in general.
Suppose \(F\) is a field and \(V\) is a finite dimensional \(F\)-vector space. Then it has a finite basis \(B\text{.}\) Let \(B'\) be any other basis, not necessarily finite. For any non-negative integer \(m\text{,}\) suppose \(C\) is any \(m\)-element subset of \(B'\text{.}\) Then \(C\) is linearly independent and so, by the Exchange Lemma, there is an \(m\)-element subset \(D\) of \(B\) such that \((B \setminus D) \cup C\) is also a basis of \(V\text{.}\) In particular, \(m \leq |B|\text{.}\) Since this holds for all \(m\text{,}\) we conclude \(|B|' \leq |B|\text{.}\) By symmetry, \(|B| \leq |B|'\) and hence \(|B|= |B'|\text{.}\)
Example13.50.Dimension of \(F^n\) in \(F\).
\(\dim_F(F^n)=|\{e_1,e_2,\dots,e_n\}|=n.\)
Example13.51.\(\R^\N\).
Consider \(V = \R^\N=\R\times \R\times \R \times \dots\text{,}\) and define rules for addition and scaling degree-wise in the evident way.
It is not hard to see \(V\) is a \(\R\)-vector space. It can be identified with the collection of all sequences \(\{a_n\}\) of real numbers. One might be interested in a basis for this vector space. At first glance the most obvious choice would be \(E=\{e_1,e_2,\ldots\}\text{,}\) where \(e_i\) is the sequence with a \(1\) in the \(i\)-th position and \(0\)’s everywhere else.
However, this set does not span \(V\) as \(v = (1,1,\ldots)\) can not be represented as a finite linear combination of these elements. (It turns out that \(E\) is the basis for the direct sum \(\bigoplus_{i\in \N}\R\text{,}\) which may be identified with all sequences having only a finite number of non-zero terms.)
Now, since we know since \(v\) is not in \(\Span(E)\text{,}\) we have that \(E \cup \{v\}\) is a linearly independent set. However, this does not span either as \((1, 2, 3, 4, \dots)\) is not in the span of this set. We know that \(V\) has a basis, but it can be shown that no countable collection of vectors forms a basis for this space, in fact \(\dim_\R \R^\N =|\R|\text{.}\) An explicit basis of this vector space is impossible to describe.
SubsectionClassification Theorem and Rank-Nullity
“O, my offence is rank, it smells to heaven.”
―William Shakespeare
Theorem13.52.Classification of Finite Dimensional Vector Spaces.
Let \(F\) be a field.
Every finite dimensional vector space \(V\) over \(F\) is isomorphic to \(F^{n}\) for \(n=\operatorname{dim}_{F}(V)\text{.}\)
For any \(m, n \in \mathbb{Z}_{\geqslant 0}, F^{m} \cong F^{n}\) if and only if \(m=n\text{.}\)
Proof.
Let \(V\) be a finite dimensional \(F\)-vector space. Then \(F\) has a finite spanning set \(S\) and by Theorem 13.40 there is a basis \(B \subseteq S\) for \(V\text{.}\) Notice that \(B\) is necessarily finite and \(V=F B\text{.}\) Set \(|B|=n\) and \(B=\left\{b_{1}, \ldots, b_{n}\right\}\text{.}\) By the UMP for Free \(R\)-Modules, there is a linear transformation \(f: F^{n} \rightarrow V\) such that \(f\left(e_{i}\right)=b_{i}\) as well as a linear transformation \(g: V \rightarrow F^{n}\) such that \(g\left(b_{i}\right)=e_{i}\text{.}\) Then both \(f \circ g: V \rightarrow V\) and \(g \circ f: F^{n} \rightarrow F^{n}\) are linear transformation which agree with the identity map on a basis. Hence by the uniqueness part of UMP for Free \(R\)-Modules we have \(f \circ g=\mathrm{id}_{V}\) and \(g \circ f=\mathrm{id}_{F^{n}}\text{.}\) Therefore, these maps are the desired isomorphisms.
Let \(\varphi: F^{m} \cong F^{n}\) be a vector space isomorphism and let \(B\) be a basis of \(F^{m}\text{.}\) We claim that \(\varphi(B)\) is a basis for \(F^{n}\text{.}\) Indeed, if
since \(\varphi\) is injective. But \(B\) is linearly independent, so we must have \(c_{i}=0\) for all \(1 \leqslant i \leqslant m\text{.}\) If \(v \in F^{n}\text{,}\) then since \(B\) spans \(F^{m}\) we have
Corollary13.53.Finite Dimensional Vector Spaces over the same Field.
Two finite dimensional vector spaces \(V\) and \(V^{\prime}\) over the same field \(F\) are isomorphic if and only if \(\operatorname{dim}_{F}(V)=\operatorname{dim}_{F}\left(V^{\prime}\right)\text{.}\)
Proof.
By the Classification of Finite Dimensional Vector Spaces, \(V\) and \(V^{\prime}\) are both of the form \(V \cong F^{m}\) and \(V^{\prime} \cong F^{n}\text{,}\) while \(F^{m} \cong F^{n}\) if and only if \(m=n\text{.}\)
We now deduce some formulas that relate the dimensions of various vector spaces.
Theorem13.54.Dimension and Subspaces.
Let \(F\) be a field and let \(W\) be a subspace of a finite dimensional \(F\)-vector space \(V\text{.}\) Then
Pick a basis \(X\) of \(W\text{.}\) Regarded as a subset of \(V\text{,}\)\(X\) remains linearly independent and thus it may be extended to a basis of \(V\) by Corollary . Let us write this basis of \(V\) as \(X \cup Y\) with \(X \cap Y = \emptyset\text{.}\)
Let \(Z := \{y + W \mid y \in Y\} \subseteq V/W\text{.}\) I claim that \(Z\) is a basis of \(V/W\text{.}\)
Given \(v + W\) we have \(v = \sum_i a_i x_i + \sum_j b_j y_j\) for some \(x_i \in X, y_j \in Y\) and scalars \(a_i, b_j\text{.}\) Since \(x + V = 0\) for all \(x \in X\text{,}\) we have \(v + W = \sum_j b_j z_j\text{.}\) This proves \(Z\) spans. Say \(\sum_j c_j y_j + W = 0\) for some \(y_1, \dots, y_m \in Y\text{.}\) Then \(\sum_j c_j y_j \in W\) and hence \(\sum_j c_j y_j = \sum_i a_i x_i\text{,}\) whence \(\sum_j c_j y_j + \sum_i - a_i x_i = 0\text{.}\) Since \(X \cup Y\) is linearly independent, \(c_j = 0\) and \(a_i = 0\) for all \(j, i\text{.}\) This proves \(Z\) is linearly independent.
Let \(F\) be a field and \(f: V \to W\) an \(F\)-linear transformation between \(F\)-vector spaces \(V\) and \(W\text{,}\) and assume \(V\) is finite dimensional. Then
Suitably interpreted, this is valid even if \(V\) is infinite dimensional.
Qual Watch.
Proving Theorem 13.57 was [cross-reference to target(s) "may-2022-5" missing or not unique] on the [cross-reference to target(s) "may-2022" missing or not unique] qualifying exam.
Exploration13.2.
Let \(R\) be a commutative ring with \(1\neq 0\text{,}\) and let \(f : R^m\to R^n\) be a surjective homomorphism of free \(R\)-modules. Prove that \(m \leq n\text{.}\)
Solution.
Let \(I\) be a maximal ideal in \(R\text{.}\) Thus \(R/I\) is a field. Lemma 1.58 tells us that \(M/IM\) and \(N/IN\) are \(R/I\)-vector spaces. Additionally, this gives rise to \(\overline{p}:M/IM\to N/IN\text{,}\) which is a surjective \(R/I\)-module linear transformation.
Note that \(M\) is generated by \(e_i+IM\) for \(i\leq m\text{.}\) Let \(a_i\in R\) and consider \(\sum a_ie_i=0\text{.}\) For this to be \(0\) we need it to be in \(IM\text{,}\) and thus all \(a_i\in I\text{.}\) So the set of \(e_i\) is a basis for \(M/IM\) with \(m\) elements. Likewise \(N/IN\) has a basis with \(n\) elements. As we are surjective, Rank\(=n\text{,}\)\(\dim=m\text{.}\) So by Rank-Nullity \(\dim(\ker)=m-n\) which is only positive with \(m\geq n\text{.}\)
Qual Watch.
Proving Exploration 13.2 was [provisional cross-reference: may-2022-5] on the [provisional cross-reference: may-2022] qualifying exam.
Summary
Vector spaces are modules where the ring of scalars is a field. They utilize several different notations than modules, which are listed in Table 13.36.
One of the main differences between vector spaces and modules is that every vector space has a basis and is therefore free.
Any two bases of the same vector space have the same dimension. Also, the Rank-Nullity Theorem is quite useful.