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,,𝑎𝑛𝐹 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)2 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)2 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 𝐻𝑉. Then 𝐻 is a subspace of 𝑉 if 𝑉 is closed under linear combinations, i.e. for all 𝐮,𝐯𝐻 and all scalars 𝛼,𝛽𝐹, we have:

𝛼𝐮+𝛽𝐯𝐻.
Proof: Alternative Subspace Criteria.

Suppose 𝑉 is a vector space over 𝐹, and let 𝐻𝑉. 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 𝟎𝑉𝐻.
  2. Closure under vector addition. Let 𝐮,𝐯𝐻. Then 𝐮+𝐯 is a linear combination of 𝐮 and 𝐯 with weights 𝛼=𝛽=1𝐹, so 𝐮+𝐯𝐻.
  3. Closure under scalar multiplication. Let 𝐯𝐻 and 𝑘𝐹. Then 𝑘𝐯 is a linear combination of 𝐯 with weight 𝑘, so 𝑘𝐯𝐻. ∎

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,,𝑎𝑛𝐹}.

Theorem 12: Spans are Subspaces

Let 𝑉 be a vector space, and let 𝑆𝑉. 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,𝐯span(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+𝑏𝑖𝐹 for all 𝑖. Thus, 𝐮+𝐯 is a linear combination of 𝑆, so 𝐮+𝐯span(𝑆).

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

    𝐯=𝑖=1𝑛𝑎𝑖𝐯𝑖.

    Let 𝑘𝐹. Then:

    𝑘𝐯=𝑘𝑖=1𝑛𝑎𝑖𝐯𝑖=𝑖=1𝑛(𝑘𝑎𝑖)𝐯𝑖.

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

Example.

Let 𝑉=3 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}={(𝑎1,𝑎2,0)|𝑎1,𝑎2}.

By Theorem 12, 𝐻 is a subspace of 𝑉.

Terminology: Spanning Set

Let 𝑉 be a vector space and let 𝑆𝑉. 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 2.

By the definition of a spanning set, we must show that span{(1,0),(0,1)}=2.

Let (𝑥,𝑦)span{(1,0),(0,1)}. Then (𝑥,𝑦)2 since taking linear combinations of vectors in 2 must yield vectors in 2 by , so span{(1,0),(0,1)}2.

Let (𝑥,𝑦) be an arbitrary vector in 2. 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, 2span{(1,0),(0,1)}. By double containment, span{(1,0),(0,1)}=2. ∎

Nonexample.

The set of vectors {(1,1),(2,2)} does not span 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, 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:

𝑆𝑉 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 𝐯𝑘𝑆 is a linear combination of the other vectors in 𝑆, then the set formed from 𝑆 by removing 𝐯𝑘 still spans 𝐻. Formally:

𝐯𝑘𝑆 s.t. 𝐯𝑘span(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(𝑆1𝑆2)=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 𝐯span(𝑆1𝑆2). Then there exist weights 𝑎1,𝑎2,,𝑎𝑛𝐹 and vectors 𝐮1,𝐮2,,𝐮𝑛𝑆1𝑆2 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,,𝐭𝑛𝑘. Then we can rewrite the equation above as follows:

𝐯=𝑖=1𝑘𝑎𝑖𝐬𝑖+𝑗=1𝑛𝑘𝑎𝑘+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 𝐡1𝐻1 and 𝐡2𝐻2.

Therefore, 𝐯𝐻1+𝐻2, so span(𝑆1𝑆2)𝐻1+𝐻2.

Conversely, suppose that 𝐯𝐻1+𝐻2. Then there exist vectors 𝐡1𝐻1 and 𝐡2𝐻2 such that:

𝐯=𝐡1+𝐡2.

Since 𝐡1𝐻1=span(𝑆1), there exist weights 𝑎1,𝑎2,,𝑎𝑘𝐹 and vectors 𝐬1,𝐬2,,𝐬𝑘𝑆1 such that:

𝐡1=𝑖=1𝑘𝑎𝑖𝐬𝑖.

Similarly, since 𝐡2𝐻2=span(𝑆2), there exist weights 𝑏1,𝑏2,,𝑏𝑛𝑘𝐹 and vectors 𝐭1,𝐭2,,𝐭𝑛𝑘𝑆2 such that:

𝐡2=𝑗=1𝑛𝑘𝑏𝑗𝐭𝑗.

Thus, we can write:

𝐯=𝑖=1𝑘𝑎𝑖𝐬𝑖+𝑗=1𝑛𝑘𝑏𝑗𝐭𝑗.

The vectors 𝐬1,𝐬2,,𝐬𝑘 and 𝐭1,𝐭2,,𝐭𝑛𝑘 are all in 𝑆1𝑆2, so 𝐯 is a linear combination of vectors in 𝑆1𝑆2. Therefore, 𝐯span(𝑆1𝑆2), so 𝐻1+𝐻2span(𝑆1𝑆2).

By double containment, span(𝑆1𝑆2)=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=1𝑛𝑏𝑖𝐯𝑖𝑎1=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.

𝒑𝒒. 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.

𝒒𝒑. 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(𝑎𝑖𝑏𝑖)𝐯𝑖=𝟎𝑉.

By our assumption, the only solution to this equation is the trivial solution, so 𝑎𝑖𝑏𝑖=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 𝑝𝑞 and 𝑞𝑝, we have 𝑝𝑞. ∎

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 𝟎𝑆, then 𝑆 is linearly dependent.

Theorem 17: Characterization of Linearly Dependent Sets

An indexed set 𝑆={𝐯1,𝐯2,,𝐯𝑛} with 𝐯1𝟎 is linearly dependent iff there exists some 𝑘>1 such that 𝐯𝑘 is a linear combination of the preceding vectors, i.e. 𝐯𝑘span{𝐯1,,𝐯𝑘1}.

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 𝐯1𝟎. Let 𝑝 be the statement that 𝑆 is linearly dependent, and let 𝑞 be the statement that there exists some 𝑘>1 such that 𝐯𝑘span{𝐯1,,𝐯𝑘1}.

𝒑𝒒. Assume 𝑆 is linearly dependent. Then there exist nontrivial weights 𝑎1,𝑎2,,𝑎𝑛 such that:

𝑖=1𝑛𝑎𝑖𝐯𝑖=𝟎𝑉.

Since 𝐯1𝟎𝑉, we can choose weights such that 𝑎𝑘0𝐹 and 𝑎𝑖=0𝐹 for all 𝑖>𝑘 for some 1<𝑘𝑛. Then we can rearrange the equation as follows:

𝑎𝑘𝐯𝑘=𝑖=1𝑘1𝑎𝑖𝐯𝑖.

Since 𝑎𝑘0𝐹, we can multiply both sides of the equation by 𝑎𝑘1 to get:

𝐯𝑘=𝑖=1𝑘1(𝑎𝑘1𝑎𝑖)𝐯𝑖.

The right-hand side of this equation is a linear combination of the vectors 𝐯1,,𝐯𝑘1, so 𝐯𝑘span{𝐯1,,𝐯𝑘1}.

𝒒𝒑. Assume that there exists some 𝑘>1 such that 𝐯𝑘span{𝐯1,,𝐯𝑘1}. By the definition of span, there exist weights 𝑎1,𝑎2,,𝑎𝑘1 such that:

𝐯𝑘=𝑖=1𝑘1𝑎𝑖𝐯𝑖.

We can rearrange this equation as follows:

𝑖=1𝑘1𝑎𝑖𝐯𝑖𝐯𝑘=𝟎𝑉𝑖=1𝑘1𝑎𝑖𝐯𝑖+(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 𝑝𝑞 and 𝑞𝑝, we have 𝑝𝑞. ∎

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 𝑆2𝑆1, 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 𝑆2𝑆1, 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 𝐯1𝟎 is linearly independent iff for every 𝑘>1, 𝐯𝑘span{𝐯1,,𝐯𝑘1}.

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, 𝑆𝑆, 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 𝐯𝑘𝑆, 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 𝐯𝑉, there exists a finite subset 𝐾 of 𝑆 such that 𝐯span(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 , and 𝑆 spans .

Exercises

Exercise 23.
Let 𝐯1,𝐯2,,𝐯𝑛 be vectors in a vector space 𝑉, and 𝑛1. Show that if 𝐯𝑛span{𝐯1,,𝐯𝑛1}, then span{𝐯1,,𝐯𝑛1}=span{𝐯1,,𝐯𝑛}.
Exercise 24.
Let 𝑆 and 𝑇 be subsets of a vector space 𝑉. Show that if 𝑆𝑇, then span(𝑆)span(𝑇).
Exercise 25.

Suppose 𝑆={𝐯1,𝐯2,,𝐯𝑘} and 𝐰1,𝐰2,,𝐰𝑙span(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 𝐰span{𝐮,𝐯} but 𝐰span{𝐮}, then 𝐯span{𝐮,𝐰}.
Exercise 27.
Show that if 𝐮 and 𝐯 are linearly independent in a vector space 𝑉, then 𝐮+𝐯 and 𝐮𝐯 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 𝐰span{𝐯1,𝐯2,,𝐯𝑛}.
Exercise 30.
Let 𝑎1,𝑎2,,𝑎𝑛 be distinct scalars in . Show that the set of functions {𝑒𝑎1𝑥,𝑒𝑎2𝑥,,𝑒𝑎𝑛𝑥} is linearly independent in 𝐶(), the set of continuous real-valued functions.
Exercise 31.

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

𝐻={(1,𝑏,𝑏2,𝑏3,)|𝑏}.
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 𝑆𝑉. Show that span(𝑆) is the smallest subspace of 𝑉 containing 𝑆. That is, 𝑆span(𝑆), and if 𝐻 is any subspace of 𝑉 such that 𝑆𝐻, 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 𝑚𝑛 and we can replace 𝑚 vectors in 𝑊 with the 𝑚 vectors from 𝑈 to get a new spanning set for 𝑉.