Jaysen Tsao
Linear Algebra ›

Span and Linear Independence

Linear Combinations

A vector space is closed under vector addition and scalar multiplication. We can generalize this property by defining the notion of a linear combination.

Definition 13: Linear Combination

Let 𝑉 be a vector space over a field 𝐹, and let 𝑆={𝐯1,𝐯2,…,𝐯𝑛}⊆𝑉. A vector 𝐰 is a linear combination of 𝑆 iff there exist scalars 𝑎1,𝑎2,…,𝑎𝑛∈𝐹 such that:

𝐰=𝑎1𝐯1+𝑎2𝐯2+⋯+𝑎𝑛𝐯𝑛=∑𝑖=1𝑛𝑎𝑖𝐯𝑖.

The scalars 𝑎1,𝑎2,…,𝑎𝑛 are called the weights of the linear combination.

That is, a linear combination of some set of vectors 𝑉 is the sum of certain scalar multiples of vectors in 𝑉. Since a vector space is closed under vector addition and scalar multiplication, any linear combination of vectors in 𝑉 must itself be a vector in 𝑉.

Corollary 4

Any linear combination of a set of vectors from 𝑉 is itself a vector in 𝑉.

Example.

The vector (5,3)∈ℝ2 is a linear combination of the vectors (1,0) and (0,1), since we can write:

(5,3)=5⋅(1,0)+3⋅(0,1).
Nonexample.

The vector (5,3)∈ℝ2 is not a linear combination of the vectors (1,1) and (2,2), since any linear combination of these two vectors must be of the form:

𝑎1⋅(1,1)+𝑎2⋅(2,2)=(𝑎1+2𝑎2,𝑎1+2𝑎2).

The two components of this vector must be equal, which is not the case for (5,3).

Terminology

A linear combination is trivial if all of its weights are zero, and nontrivial otherwise. A trivial linear combination, then, is always equal to 𝟎.

Theorem 11: Alternative Subspace Criteria

Let 𝑉 be a vector space over a field 𝐹, and let 𝐻⊆𝑉. Then 𝐻 is a subspace of 𝑉 if 𝑉 is closed under linear combinations, i.e. for all 𝐮,𝐯∈𝐻 and all scalars 𝛼,𝛽∈𝐹, we have:

𝛼𝐮+𝛽𝐯∈𝐻.
Proof: Alternative Subspace Criteria.

Suppose 𝑉 is a vector space over 𝐹, and let 𝐻⊆𝑉. Also suppose that 𝐻 is closed under linear combinations. We will show that 𝐻 satisfies the three Subspace Criteria.

  1. Existence of additive identity. 𝟎𝑉 is the trivial linear combination, so 𝟎𝑉∈𝐻.
  2. Closure under vector addition. Let 𝐮,𝐯∈𝐻. Then 𝐮+𝐯 is a linear combination of 𝐮 and 𝐯 with weights 𝛼=𝛽=1𝐹, so 𝐮+𝐯∈𝐻.
  3. Closure under scalar multiplication. Let 𝐯∈𝐻 and 𝑘∈𝐹. Then 𝑘𝐯 is a linear combination of 𝐯 with weight 𝑘, so 𝑘𝐯∈𝐻. ∎

Spans and Spanning Sets

Clearly, there are vectors that can be written as linear combinations of some set of vectors, but there may also exist vectors that can’t. The span of a set of vectors describes the set of all vectors that can be written as linear combinations of that set:

Definition 14: Span

Let 𝑉 be a vector space over a field 𝐹, and let 𝑆={𝐯1,𝐯2,…,𝐯𝑛}⊆𝑉. The span of 𝑆, denoted span(𝑆) or span{𝐯1,𝐯2,…,𝐯𝑛}, is the set of all linear combinations of 𝑆:

span(𝑆)={∑𝑖=1𝑛𝑎𝑖𝐯𝑖|𝑎1,𝑎2,…,𝑎𝑛∈𝐹}.

Theorem 12: Spans are Subspaces

Let 𝑉 be a vector space, and let 𝑆⊆𝑉. Then span(𝑆) is a subspace of 𝑉.

Proof: Spans are Subspaces.

Let 𝑉 be a vector space over 𝐹, and let 𝑆={𝐯1,𝐯2,…,𝐯𝑛}⊆𝑉. Checking the three Subspace Criteria:

  1. Existence of additive identity. Fix all weights 𝑎1=𝑎2=…=𝑎𝑛=0𝐹. Then for any linear combination of 𝑆, we have:

    ∑𝑖=1𝑛𝑎𝑖𝐯𝑖=∑𝑖=1𝑛0𝐹𝐯𝑖=∑𝑖=1𝑛𝟎𝑉=𝟎𝑉.
  2. Closure under vector addition. Let 𝐮,𝐯∈span(𝑆), so there exist weights 𝑎1,𝑎2,…,𝑎𝑛 and 𝑏1,𝑏2,…,𝑏𝑛 such that:

    𝐮=∑𝑖=1𝑛𝑎𝑖𝐯𝑖,𝐯=∑𝑖=1𝑛𝑏𝑖𝐯𝑖.

    Then:

    𝐮+𝐯=∑𝑖=1𝑛𝑎𝑖𝐯𝑖+∑𝑖=1𝑛𝑏𝑖𝐯𝑖=∑𝑖=1𝑛(𝑎𝑖+𝑏𝑖)𝐯𝑖.

    𝐹 is closed under field addition, so 𝑎𝑖+𝑏𝑖∈𝐹 for all 𝑖. Thus, 𝐮+𝐯 is a linear combination of 𝑆, so 𝐮+𝐯∈span(𝑆).

  3. Closure under scalar multiplication. Let 𝐯∈span(𝑆), so there exist weights 𝑎1,𝑎2,…,𝑎𝑛 such that:

    𝐯=∑𝑖=1𝑛𝑎𝑖𝐯𝑖.

    Let 𝑘∈𝐹. Then:

    𝑘𝐯=𝑘∑𝑖=1𝑛𝑎𝑖𝐯𝑖=∑𝑖=1𝑛(𝑘𝑎𝑖)𝐯𝑖.

    𝐹 is closed under field multiplication, so 𝑘𝑎𝑖∈𝐹 for all 𝑖. Thus, 𝑘𝐯 is a linear combination of 𝑆, so 𝑘𝐯∈span(𝑆). ∎

Example.

Let 𝑉=ℝ3 and let 𝑆={(1,0,0),(0,1,0)}⊆𝑉. Define 𝐻=span(𝑆), so:

𝐻={𝑎1⋅(1,0,0)+𝑎2⋅(0,1,0)|𝑎1,𝑎2∈ℝ}={(𝑎1,𝑎2,0)|𝑎1,𝑎2∈ℝ}.

By Theorem 12, 𝐻 is a subspace of 𝑉.

Terminology: Spanning Set

Let 𝑉 be a vector space and let 𝑆⊆𝑉. All of the following statements are equivalent:

  • span(𝑆)=𝑉.
  • 𝑆 is a spanning set for 𝑉.
  • 𝑆 spans 𝑉.
  • 𝑉 is the subspace generated by 𝑆.

That is, to say that “𝑆 spans 𝑉” is to say that every vector in 𝑉 can be written as a linear combination of vectors in 𝑆.

Example: Show that the set of vectors {(1,0),(0,1)} spans ℝ2.

By the definition of a spanning set, we must show that span{(1,0),(0,1)}=ℝ2.

Let (𝑥,𝑦)∈span{(1,0),(0,1)}. Then (𝑥,𝑦)∈ℝ2 since taking linear combinations of vectors in ℝ2 must yield vectors in ℝ2 by , so span{(1,0),(0,1)}⊆ℝ2.

Let (𝑥,𝑦) be an arbitrary vector in ℝ2. Then we can write the vector (𝑥,𝑦) as a linear combination of the vectors (1,0) and (0,1) as follows:

(𝑥,𝑦)=𝑥⋅(1,0)+𝑦⋅(0,1).

So, ℝ2⊆span{(1,0),(0,1)}. By double containment, span{(1,0),(0,1)}=ℝ2. ∎

Nonexample.

The set of vectors {(1,1),(2,2)} does not span ℝ2, since any linear combination of these two vectors must be of the form:

𝑎1⋅(1,1)+𝑎2⋅(2,2)=(𝑎1+2𝑎2,𝑎1+2𝑎2).

The two components of this vector must be equal, so there is no way to write a vector like (5,3) as a linear combination of these two vectors.

Important

The empty set ∅ spans the trivial subspace {𝟎}.

Theorem 13: Subspaces are Spans

Let 𝑉 be a vector space, and let 𝐻 be a subspace of 𝑉. Then there exists a finite subset of 𝑉 that spans 𝐻:

∃𝑆⊆𝑉 such that span(𝑆)=𝐻.

That is, we can take vectors from 𝑉 to generate the subspace 𝐻.

Proof: Subspaces are Spans.

Let 𝑉 be a vector space, and let 𝐻 be a subspace of 𝑉. If 𝐻={𝟎𝑉}, then we can take 𝑆=∅, so assume that 𝐻≠{𝟎𝑉}.

TODO ∎

Theorem 14: Spanning Set Theorem

Let 𝑆={𝐯1,𝐯2,…,𝐯𝑛}, and let 𝐻=span(𝑆) be the subspace generated by 𝑆. Then if some vector 𝐯𝑘∈𝑆 is a linear combination of the other vectors in 𝑆, then the set formed from 𝑆 by removing 𝐯𝑘 still spans 𝐻. Formally:

∃𝐯𝑘∈𝑆 s.t. 𝐯𝑘∈span(𝑆\{𝐯𝑘})⇒span(𝑆\{𝐯𝑘})=span(𝑆).
Proof: Spanning Set Theorem.

Theorem 15: Union of Spanning Sets

Let 𝑆1 and 𝑆2 be sets of vectors, and let 𝐻1=span(𝑆1) and 𝐻2=span(𝑆2) be the subspaces generated by 𝑆1 and 𝑆2, respectively. Then the union of 𝑆1 and 𝑆2 spans the sum of 𝐻1 and 𝐻2. Formally:

span(𝑆1∪𝑆2)=𝐻1+𝐻2.
Proof: Theorem 15.

Let 𝑆1 and 𝑆2 be sets of vectors, and let 𝐻1=span(𝑆1) and 𝐻2=span(𝑆2) be the subspaces generated by 𝑆1 and 𝑆2, respectively.

Suppose 𝐯∈span(𝑆1∪𝑆2). Then there exist weights 𝑎1,𝑎2,…,𝑎𝑛∈𝐹 and vectors 𝐮1,𝐮2,…,𝐮𝑛∈𝑆1∪𝑆2 such that:

𝐯=∑𝑖=1𝑛𝑎𝑖𝐮𝑖.

We can partition the vectors 𝐮1,𝐮2,…,𝐮𝑛 into two groups: those that are in 𝑆1 and those that are in 𝑆2. Let the vectors in the first group be denoted by 𝐬1,𝐬2,…,𝐬𝑘, and let the vectors in the second group be denoted by 𝐭1,𝐭2,…,𝐭𝑛−𝑘. Then we can rewrite the equation above as follows:

𝐯=∑𝑖=1𝑘𝑎𝑖𝐬𝑖+∑𝑗=1𝑛−𝑘𝑎𝑘+𝑗𝐭𝑗.

The first sum is a linear combination of vectors in 𝑆1, so the first sum is a vector in span(𝑆1)=𝐻1; call it 𝐡1. Similarly, the second sum is a linear combination of vectors in 𝑆2, so the second sum is a vector in span(𝑆2)=𝐻2; call it 𝐡2. Thus, we can write:

𝐯=𝐡1+𝐡2, where 𝐡1∈𝐻1 and 𝐡2∈𝐻2.

Therefore, 𝐯∈𝐻1+𝐻2, so span(𝑆1∪𝑆2)⊆𝐻1+𝐻2.

Conversely, suppose that 𝐯∈𝐻1+𝐻2. Then there exist vectors 𝐡1∈𝐻1 and 𝐡2∈𝐻2 such that:

𝐯=𝐡1+𝐡2.

Since 𝐡1∈𝐻1=span(𝑆1), there exist weights 𝑎1,𝑎2,…,𝑎𝑘∈𝐹 and vectors 𝐬1,𝐬2,…,𝐬𝑘∈𝑆1 such that:

𝐡1=∑𝑖=1𝑘𝑎𝑖𝐬𝑖.

Similarly, since 𝐡2∈𝐻2=span(𝑆2), there exist weights 𝑏1,𝑏2,…,𝑏𝑛−𝑘∈𝐹 and vectors 𝐭1,𝐭2,…,𝐭𝑛−𝑘∈𝑆2 such that:

𝐡2=∑𝑗=1𝑛−𝑘𝑏𝑗𝐭𝑗.

Thus, we can write:

𝐯=∑𝑖=1𝑘𝑎𝑖𝐬𝑖+∑𝑗=1𝑛−𝑘𝑏𝑗𝐭𝑗.

The vectors 𝐬1,𝐬2,…,𝐬𝑘 and 𝐭1,𝐭2,…,𝐭𝑛−𝑘 are all in 𝑆1∪𝑆2, so 𝐯 is a linear combination of vectors in 𝑆1∪𝑆2. Therefore, 𝐯∈span(𝑆1∪𝑆2), so 𝐻1+𝐻2⊆span(𝑆1∪𝑆2).

By double containment, span(𝑆1∪𝑆2)=𝐻1+𝐻2. ∎

Linear Independence

For some set of vectors 𝑆, there may be multiple configurations of weights to write a vector in span(𝑆). For example, if 𝑆={(1,0),(0,1),(1,1)}, then we can write the vector (1,1) as a linear combination of 𝑆 in two different ways:

0⋅(1,0)+0⋅(0,1)+1⋅(1,1)=(1,1).1⋅(1,0)+1⋅(0,1)+0⋅(1,1)=(1,1).

Some sets, called linearly independent sets, have the property that each vector in their span (except for 𝟎) can be written as a linear combination of the set with only one unique combination of weights.

Definition 15: Linear Independence

A set of vectors 𝑆={𝐯1,𝐯2,…,𝐯𝑛} is linearly independent iff each vector in span(𝑆) can be written as a linear combination of 𝑆 in only one way.

Formally 𝑆 is linearly independent iff for some sets of weights 𝑎1,𝑎2,…,𝑎𝑛 and 𝑏1,𝑏2,…,𝑏𝑛:

∑𝑖=1𝑛𝑎𝑖𝐯𝑖=∑𝑖=1𝑛𝑏𝑖𝐯𝑖⇒𝑎1=𝑏1,𝑎2=𝑏2,…,𝑎𝑛=𝑏𝑛.

We can restate Definition 15 as: to say that a set of vectors 𝑆 is linearly independent means that if two linear combinations of vectors in 𝑆 are equal, then their weights must be equal.

Definition 16: Linear Dependence

A set of vectors 𝑆 is linearly dependent iff it is not linearly independent.

Terminology

To say that vectors 𝐮 and 𝐯 are linearly independent means that the set {𝐮,𝐯} is linearly independent. Also, to say that 𝑆 is a linearly independent set in 𝑉 means that 𝑆 is a set of vectors taken from 𝑉 that is linearly independent.

Theorem 16: Formalism for Linear Independence

Let 𝑉 be a vector space over 𝐹, and let 𝑆={𝐯1,𝐯2,…,𝐯𝑛}⊆𝑉. Then 𝑆 is linearly independent if and only if the only solution to the equation:

∑𝑖=1𝑛𝑎𝑖𝐯𝑖=𝟎𝑉

is the trivial solution 𝑎1=𝑎2=⋯=𝑎𝑛=0𝐹.

Proof: Formalism for Linear Independence.

Let 𝑉 be a vector space over 𝐹, and let 𝑆={𝐯1,𝐯2,…,𝐯𝑛}⊆𝑉. Let 𝑝 be the statement that 𝑆 is linearly independent, and let 𝑞 be the statement that the only solution to the equation 𝑎1𝐯1+𝑎2𝐯2+⋯+𝑎𝑛𝐯𝑛=𝟎𝑉 is the trivial solution 𝑎1=𝑎2=⋯=𝑎𝑛=0𝐹.

𝒑⇒𝒒. Assume 𝑆 is linearly independent. Let 𝑎1,𝑎2,…,𝑎𝑛 be scalars such that:

∑𝑖=1𝑛𝑎𝑖𝐯𝑖=𝟎𝑉.

Then, by Theorem 3, realize that:

∑𝑖=1𝑛0𝐹𝐯𝑖=∑𝑖=1𝑛𝟎𝑉=𝟎𝑉, so ∑𝑖=1𝑛𝑎𝑖𝐯𝑖=∑𝑖=1𝑛0𝐹𝐯𝑖.

Since 𝑆 is linearly independent, the weights of these two linear combinations must be equal, so 𝑎1=𝑎2=…=𝑎𝑛=0𝐹. Therefore, the only solution to the equation is the trivial solution.

𝒒⇒𝒑. Assume that the only solution to the equation is the trivial solution. Let 𝑎1,𝑎2,…,𝑎𝑛 and 𝑏1,𝑏2,…,𝑏𝑛 be scalars such that:

∑𝑖=1𝑛𝑎𝑖𝐯𝑖=∑𝑖=1𝑛𝑏𝑖𝐯𝑖.

Then we can rearrange this equation as follows:

∑𝑖=1𝑛(𝑎𝑖−𝑏𝑖)𝐯𝑖=𝟎𝑉.

By our assumption, the only solution to this equation is the trivial solution, so 𝑎𝑖−𝑏𝑖=0𝐹 for all 𝑖, so 𝑎1=𝑏1, 𝑎2=𝑏2, …, 𝑎𝑛=𝑏𝑛. Therefore, if two linear combinations of vectors in 𝑆 are equal, then their weights must be equal, so 𝑆 is linearly independent.

Since 𝑝⇒𝑞 and 𝑞⇒𝑝, we have 𝑝⇔𝑞. ∎

Following this theorem, if any one of the 𝐯𝑖 in the equation is the zero vector 𝟎𝑉, then any weight can be used for that vector and the equation in Theorem 16 would still hold, so the set would be linearly dependent. Thus, we have the following corollary:

Corollary 5

If 𝟎∈𝑆, then 𝑆 is linearly dependent.

Theorem 17: Characterization of Linearly Dependent Sets

An indexed set 𝑆={𝐯1,𝐯2,…,𝐯𝑛} with 𝐯1≠𝟎 is linearly dependent iff there exists some 𝑘>1 such that 𝐯𝑘 is a linear combination of the preceding vectors, i.e. 𝐯𝑘∈span{𝐯1,…,𝐯𝑘−1}.

This theorem can be restated to say that a set of vectors 𝑆 is linearly dependent iff there is a vector in 𝑆 that can be written as a linear combination of the other vectors in 𝑆.

Proof: Theorem 17.

Let 𝑆={𝐯1,𝐯2,…,𝐯𝑛} be an indexed set of vectors with 𝐯1≠𝟎. Let 𝑝 be the statement that 𝑆 is linearly dependent, and let 𝑞 be the statement that there exists some 𝑘>1 such that 𝐯𝑘∈span{𝐯1,…,𝐯𝑘−1}.

𝒑⇒𝒒. Assume 𝑆 is linearly dependent. Then there exist nontrivial weights 𝑎1,𝑎2,…,𝑎𝑛 such that:

∑𝑖=1𝑛𝑎𝑖𝐯𝑖=𝟎𝑉.

Since 𝐯1≠𝟎𝑉, we can choose weights such that 𝑎𝑘≠0𝐹 and 𝑎𝑖=0𝐹 for all 𝑖>𝑘 for some 1<𝑘≤𝑛. Then we can rearrange the equation as follows:

𝑎𝑘𝐯𝑘=−∑𝑖=1𝑘−1𝑎𝑖𝐯𝑖.

Since 𝑎𝑘≠0𝐹, we can multiply both sides of the equation by 𝑎𝑘−1 to get:

𝐯𝑘=−∑𝑖=1𝑘−1(𝑎𝑘−1𝑎𝑖)𝐯𝑖.

The right-hand side of this equation is a linear combination of the vectors 𝐯1,…,𝐯𝑘−1, so 𝐯𝑘∈span{𝐯1,…,𝐯𝑘−1}.

𝒒⇒𝒑. Assume that there exists some 𝑘>1 such that 𝐯𝑘∈span{𝐯1,…,𝐯𝑘−1}. By the definition of span, there exist weights 𝑎1,𝑎2,…,𝑎𝑘−1 such that:

𝐯𝑘=∑𝑖=1𝑘−1𝑎𝑖𝐯𝑖.

We can rearrange this equation as follows:

∑𝑖=1𝑘−1𝑎𝑖𝐯𝑖−𝐯𝑘=𝟎𝑉∑𝑖=1𝑘−1𝑎𝑖𝐯𝑖+(−1𝐹)𝐯𝑘=𝟎𝑉.

No matter what any of the 𝑎𝑖 are, a nontrivial weight −1𝐹 is used for 𝐯𝑘 to get a linear combination that equals 𝟎𝑉, so by Theorem 16, 𝑆 is linearly dependent.

Since 𝑝⇒𝑞 and 𝑞⇒𝑝, we have 𝑝⇔𝑞. ∎

Important

Theorem 17 does not guarantee that every vector in a linearly dependent set is a linear combination of the preceding vectors; only that at least one vector is.

It follows, then, that adding vectors to a linearly dependent set results in another linearly dependent set, because if there is already a vector in the set that can be written as a linear combination of the preceding vectors, then adding more vectors won’t change that fact:

Corollary 6

If 𝑆1 is a linearly dependent set and 𝑆2⊃𝑆1, then 𝑆2 is also a linearly dependent set.

It also happens that, removing a vector from a linearly independent set results in another linearly independent set:

Proposition 5

If 𝑆1 is a linearly independent set and 𝑆2⊂𝑆1, then 𝑆2 is also a linearly independent set.

Proof: Proposition 5.
TODO

Since the empty set ∅ is a subset of every set, it follows that the empty set is linearly independent:

Corollary 7

The empty set ∅ is a linearly independent set.

Corollary 7 can also be vacuously deduced. Since any linear combination of the empty set must be the zero vector, and there is only one way to write the zero vector as a linear combination of the empty set (with no weights at all).

Also, the negation of Theorem 17 gives us a characterization of linearly independent sets:

Corollary 8: Characterization of Linearly Independent Sets

An indexed set 𝑆={𝐯1,𝐯2,…,𝐯𝑛} with 𝐯1≠𝟎 is linearly independent iff for every 𝑘>1, 𝐯𝑘∉span{𝐯1,…,𝐯𝑘−1}.

Theorem 18: Existence of Linearly Independent Subsets

Let 𝑆 be a set of vectors, and let 𝐻=span(𝑆) be the subspace generated by 𝑆. Then if 𝑆 is a linearly dependent set, then there exists a proper subset of 𝑆, 𝑆′⊂𝑆, such that 𝑆′ is linearly independent and still spans 𝐻, i.e. span(𝑆′)=span(𝑆).

Proof: Theorem 18.
Let 𝑆 be a set of vectors, and let 𝐻=span(𝑆) be the subspace generated by 𝑆. Assume that 𝑆 is a linearly dependent set. Then by Theorem 17, there exists some vector, say 𝐯𝑘∈𝑆, that can be written as a linear combination of the other vectors in 𝑆. By the Spanning Set Theorem, removing that vector from 𝑆 does not change the span of the set. We can repeat this process until we are left with a linearly independent subset of 𝑆 that still spans 𝐻. ∎

Theorem 19: Linear Dependence of 𝐹𝑛

Let 𝐹 be a field, and define 𝐹𝑛 as in Definition 7. The set 𝑆={𝐯1,𝐯2,…,𝐯𝑝}⊂𝐹𝑛 is linearly dependent in 𝐹𝑛 if 𝑆 contains more than 𝑛 vectors, i.e. if 𝑝>𝑛.

Proof: Theorem 19.

Span and Linear Independence of Infinite Sets

The previous definitions of span and linear independence build from the idea of linear combinations, which only involve finite sets of vectors.

However, we can generalize the definitions of span and linear independence to infinite sets of vectors:

Definition 17: Span of an Infinite Set

Let 𝑆 be an infinite subset of a vector space 𝑉. 𝑆 is a spanning set for 𝑉 iff for every vector 𝐯∈𝑉, there exists a finite subset 𝐾 of 𝑆 such that 𝐯∈span(𝐾).

Definition 18: Linear Independence of an Infinite Set

Let 𝑆 be an infinite subset of a vector space 𝑉. 𝑆 is linearly independent iff for every finite subset 𝐾 of 𝑆, 𝐾 is linearly independent.

Example.
The infinite set 𝑆={1,𝑥,𝑥2,𝑥3,…} is linearly independent in the vector space of all polynomials ℙ, and 𝑆 spans ℙ.

Exercises

Exercise 23.
Let 𝐯1,𝐯2,…,𝐯𝑛 be vectors in a vector space 𝑉, and 𝑛≥1. Show that if 𝐯𝑛∈span{𝐯1,…,𝐯𝑛−1}, then span{𝐯1,…,𝐯𝑛−1}=span{𝐯1,…,𝐯𝑛}.
Exercise 24.
Let 𝑆 and 𝑇 be subsets of a vector space 𝑉. Show that if 𝑆⊆𝑇, then span(𝑆)⊆span(𝑇).
Exercise 25.

Suppose 𝑆={𝐯1,𝐯2,…,𝐯𝑘} and 𝐰1,𝐰2,…,𝐰𝑙∈span(𝑆). Show that:

span{𝐰1,𝐰2,…,𝐰𝑙}⊆span(𝑆).
Exercise 26.
Let 𝐮,𝐯,𝐰 be vectors in a vector space 𝑉. Show that if 𝐰∈span{𝐮,𝐯} but 𝐰∉span{𝐮}, then 𝐯∈span{𝐮,𝐰}.
Exercise 27.
Show that if 𝐮 and 𝐯 are linearly independent in a vector space 𝑉, then 𝐮+𝐯 and 𝐮−𝐯 are also linearly independent in 𝑉.
Exercise 28.
Show that 𝐮 and 𝐯 are linearly dependent iff either 𝐮 or 𝐯 is a scalar multiple of the other, i.e. 𝐮=𝑘𝐯 or 𝐯=𝑘𝐮 for some scalar 𝑘.
Exercise 29.
Show that if {𝐯1,𝐯2,…,𝐯𝑛} is a linearly independent set in 𝑉 but {𝐯1,𝐯2,…,𝐯𝑛,𝐰} is a linearly dependent set in 𝑉, then 𝐰∈span{𝐯1,𝐯2,…,𝐯𝑛}.
Exercise 30.
Let 𝑎1,𝑎2,…,𝑎𝑛 be distinct scalars in ℝ. Show that the set of functions {𝑒𝑎1𝑥,𝑒𝑎2𝑥,…,𝑒𝑎𝑛𝑥} is linearly independent in 𝐶(ℝ), the set of continuous real-valued functions.
Exercise 31.

ℝ∞ is the set of all infinite sequences of real numbers. Show that 𝐻, a subset of ℝ∞ defined as follows, is a linearly independent set in ℝ∞:

𝐻={(1,𝑏,𝑏2,𝑏3,…)∈ℝ∞|𝑏∈ℝ}.
Exercise 32: Telescoping Linear Independence.

Let 𝑉 be a vector space, and suppose {𝐯1,𝐯2,…,𝐯𝑛} is a linearly independent set in 𝑉. Define a new set of vectors 𝑊 by:

𝑊={𝐰1,𝐰2,…,𝐰𝑛} where 𝐰𝑘=∑𝑖=1𝑘𝐯𝑖 for 𝑘=1,2,…,𝑛.

That is, 𝑊={𝐯1,𝐯1+𝐯2,…,𝐯1+𝐯2+⋯+𝐯𝑛}. Show that 𝑊 is also a linearly independent set in 𝑉.

Exercise 33.
Let 𝑆⊆𝑉. Show that span(𝑆) is the smallest subspace of 𝑉 containing 𝑆. That is, 𝑆⊆span(𝑆), and if 𝐻 is any subspace of 𝑉 such that 𝑆⊆𝐻, then span(𝑆)⊆𝐻.
Exercise 34: Steinitz Exchange Lemma.
Let 𝑉 be a vector space. Let 𝑈={𝐮1,𝐮2,…,𝐮𝑚} and 𝑊={𝐰1,𝐰2,…,𝐰𝑛} be subsets of 𝑉. Show that if 𝑈 is linearly independent and 𝑊 spans 𝑉, then 𝑚≤𝑛 and we can replace 𝑚 vectors in 𝑊 with the 𝑚 vectors from 𝑈 to get a new spanning set for 𝑉.