# Linear Algebra/Definition and Examples of Isomorphisms

We start with two examples that suggest the right definition.

- Example 1.1

Consider the example mentioned above, the space of two-wide row vectors and the space of two-tall column vectors. They are "the same" in that if we associate the vectors that have the same components, e.g.,

then this correspondence preserves the operations, for instance this addition

and this scalar multiplication.

More generally stated, under the correspondence

both operations are preserved:

and

(all of the variables are real numbers).

- Example 1.2

Another two spaces we can think of as "the same" are , the space of quadratic polynomials, and . A natural correspondence is this.

The structure is preserved: corresponding elements add in a corresponding way

and scalar multiplication corresponds also.

- Definition 1.3

An **isomorphism** between two vector spaces and is a map that

- is a correspondence: is one-to-one and onto;
^{[1]} **preserves structure:**if then

(we write , read " is isomorphic to ", when such a map exists).

("Morphism" means map, so "isomorphism" means a map expressing sameness.)

- Example 1.4

The vector space of functions of is isomorphic to the vector space under this map.

We will check this by going through the conditions in the definition.

We will first verify condition 1, that the map is a correspondence between the sets underlying the spaces.

To establish that is one-to-one, we must prove that only when . If

then, by the definition of ,

from which we can conclude that and because column vectors are equal only when they have equal components. We've proved that implies that , which shows that is one-to-one.

To check that is onto we must check that any member of the codomain is the image of some member of the domain . But that's clear—any

is the image under of .

Next we will verify condition (2), that preserves structure.

This computation shows that preserves addition.

A similar computation shows that preserves scalar multiplication.

With that, conditions (1) and (2) are verified, so we know that is an isomorphism and we can say that the spaces are isomorphic .

- Example 1.5

Let be the space of linear combinations of three variables , , and , under the natural addition and scalar multiplication operations. Then is isomorphic to , the space of quadratic polynomials.

To show this we will produce an isomorphism map. There is more than one possibility; for instance, here are four.

The first map is the more natural correspondence in that it just carries the coefficients over. However, below we shall verify that the second one is an isomorphism, to underline that there are isomorphisms other than just the obvious one (showing that is an isomorphism is Problem 3).

To show that is one-to-one, we will prove that if then . The assumption that gives, by the definition of , that . Equal polynomials have equal coefficients, so , , and . Thus implies that and therefore is one-to-one.

The map is onto because any member of the codomain is the image of some member of the domain, namely it is the image of . For instance, is .

The computations for structure preservation are like those in the prior example. This map preserves addition

and scalar multiplication.

Thus is an isomorphism and we write .

We are sometimes interested in an isomorphism of a space with itself, called an **automorphism**. An identity map is an automorphism. The next two examples show that there are others.

- Example 1.6

A **dilation** map that multiplies all vectors by a nonzero scalar is an automorphism of .

A **rotation** or **turning map** that rotates all vectors through an angle is an automorphism.

A third type of automorphism of is a map that **flips** or **reflects** all vectors over a line through the origin.

See Problem 20.

- Example 1.7

Consider the space of polynomials of degree 5 or less and the map that sends a polynomial to . For instance, under this map and . This map is an automorphism of this space; the check is Problem 12.

This isomorphism of with itself does more than just tell us that the space is "the same" as itself. It gives us some insight into the space's structure. For instance, below is shown a family of parabolas, graphs of members of . Each has a vertex at , and the left-most one has zeroes at and , the next one has zeroes at and , etc.

Geometrically, the substitution of for in any function's argument shifts its graph to the right by one. Thus, and 's action is to shift all of the parabolas to the right by one. Notice that the picture before is applied is the same as the picture after is applied, because while each parabola moves to the right, another one comes in from the left to take its place. This also holds true for cubics, etc. So the automorphism gives us the insight that has a certain horizontal homogeneity; this space looks the same near as near .

As described in the preamble to this section, we will next produce some results supporting the contention that the definition of isomorphism above captures our intuition of vector spaces being the same.

Of course the definition itself is persuasive: a vector space consists of two components, a set and some structure, and the definition simply requires that the sets correspond and that the structures correspond also. Also persuasive are the examples above. In particular, Example 1.1, which gives an isomorphism between the space of two-wide row vectors and the space of two-tall column vectors, dramatizes our intuition that isomorphic spaces are the same in all relevant respects. Sometimes people say, where , that " is just painted green"—any differences are merely cosmetic.

Further support for the definition, in case it is needed, is provided by the following results that, taken together, suggest that all the things of interest in a vector space correspond under an isomorphism. Since we studied vector spaces to study linear combinations, "of interest" means "pertaining to linear combinations". Not of interest is the way that the vectors are presented typographically (or their color!).

As an example, although the definition of isomorphism doesn't explicitly say that the zero vectors must correspond, it is a consequence of that definition.

- Lemma 1.8

An isomorphism maps a zero vector to a zero vector.

- Proof

Where is an isomorphism, fix any . Then .

The definition of isomorphism requires that sums of two vectors correspond and that so do scalar multiples. We can extend that to say that all linear combinations correspond.

- Lemma 1.9

For any map between vector spaces these statements are equivalent.

- preserves structure
- preserves linear combinations of two vectors
- preserves linear combinations of any finite number of vectors

- Proof

Since the implications and are clear, we need only show that . Assume statement 1. We will prove statement 3 by induction on the number of summands .

The one-summand base case, that , is covered by the assumption of statement 1.

For the inductive step assume that statement 3 holds whenever there are or fewer summands, that is, whenever , or , ..., or . Consider the -summand case. The first half of 1 gives

by breaking the sum along the final "". Then the inductive hypothesis lets us break up the -term sum.

Finally, the second half of statement 1 gives

when applied times.

In addition to adding to the intuition that the definition of isomorphism does indeed preserve the things of interest in a vector space, that lemma's second item is an especially handy way of checking that a map preserves structure.

We close with a summary. The material in this section augments the chapter on Vector Spaces. There, after giving the definition of a vector space, we informally looked at what different things can happen. Here, we defined the relation "" between vector spaces and we have argued that it is the right way to split the collection of vector spaces into cases because it preserves the features of interest in a vector space—in particular, it preserves linear combinations. That is, we have now said precisely what we mean by "the same", and by "different", and so we have precisely classified the vector spaces.

## Exercises[edit]

*This exercise is recommended for all readers.*

- Problem 1

Verify, using Example 1.4 as a model, that the two correspondences given before the definition are isomorphisms.

*This exercise is recommended for all readers.*

- Problem 2

For the map given by

Find the image of each of these elements of the domain.

Show that this map is an isomorphism.

- Problem 3

Show that the natural map from Example 1.5 is an isomorphism.

*This exercise is recommended for all readers.*

- Problem 4

Decide whether each map is an isomorphism (if it is an isomorphism then prove it and if it isn't then state a condition that it fails to satisfy).

- given by
- given by
- given by
- given by

- Problem 5

Show that the map given by is one-to-one and onto.Is it an isomorphism?

*This exercise is recommended for all readers.*

- Problem 6

Refer to Example 1.1. Produce two more isomorphisms (of course, that they satisfy the conditions in the definition of isomorphism must be verified).

- Problem 7

Refer to Example 1.2. Produce two more isomorphisms (and verify that they satisfy the conditions).

*This exercise is recommended for all readers.*

- Problem 8

Show that, although is not itself a subspace of , it is isomorphic to the -plane subspace of .

- Problem 9

Find two isomorphisms between and .

*This exercise is recommended for all readers.*

- Problem 10

For what is isomorphic to ?

- Problem 11

For what is isomorphic to ?

- Problem 12

Prove that the map in Example 1.7, from to given by , is a vector space isomorphism.

- Problem 13

Why, in Lemma 1.8, must there be a ? That is, why must be nonempty?

- Problem 14

Are any two trivial spaces isomorphic?

- Problem 15

In the proof of Lemma 1.9, what about the zero-summands case (that is, if is zero)?

- Problem 16

Show that any isomorphism has the form for some nonzero real number .

*This exercise is recommended for all readers.*

- Problem 17

These prove that isomorphism is an equivalence relation.

- Show that the identity map is an isomorphism. Thus, any vector space is isomorphic to itself.
- Show that if is an isomorphism then so is its inverse . Thus, if is isomorphic to then also is isomorphic to .
- Show that a composition of isomorphisms is an isomorphism: if is an isomorphism and is an isomorphism then so also is . Thus, if is isomorphic to and is isomorphic to , then also is isomorphic to .

- Problem 18

Suppose that preserves structure. Show that is one-to-one if and only if the unique member of mapped by to is .

- Problem 19

Suppose that is an isomorphism. Prove that the set is linearly dependent if and only if the set of images is linearly dependent.

*This exercise is recommended for all readers.*

- Problem 20

Show that each type of map from Example 1.6 is an automorphism.

- Dilation by a nonzero scalar .
- Rotation through an angle .
- Reflection over a line through the origin.

*Hint.* For the second and third items, polar coordinates are useful.

- Problem 21

Produce an automorphism of other than the identity map, and other than a shift map .

- Problem 22

- Show that a function is an automorphism if and only if it has the form for some .
- Let be an automorphism of such that . Find .
- Show that a function is an automorphism if and only if it has the form
*Hint.*Exercises in prior subsections have shown that - Let be an automorphism of with

- Problem 24

We show that isomorphisms can be tailored to fit in that, sometimes, given vectors in the domain and in the range we can produce an isomorphism associating those vectors.

- Let be a basis for so that any has a unique representation as , which we denote in this way.
- Show that this function is one-to-one and onto.
- Show that it preserves structure.
- Produce an isomorphism from to that fits these specifications.

- Problem 25

Prove that a space is -dimensional if and only if it is isomorphic to . *Hint.* Fix a basis for the space and consider the map sending a vector over to its representation with respect to .

- Problem 26

*(Requires the subsection on Combining Subspaces, which is optional.)* Let and be vector spaces. Define a new vector space, consisting of the set along with these operations.

This is a vector space, the **external direct sum** of and .

- Check that it is a vector space.
- Find a basis for, and the dimension of, the external direct sum .
- What is the relationship among , , and ?
- Suppose that and are subspaces of a vector space such that (in this case we say that is the
**internal direct sum**of and ). Show that the map given by