A complex number is algebraic if it is the root of some polynomial
with rational coefficients.
is algebraic (e.g. the polynomial
);
is algebraic (e.g. the polynomial
);
and
are not. A complex number that is not algebraic is called transcendental.
Is the sum of algebraic numbers always algebraic? What about the product of algebraic numbers? For example, given that
and
are algebraic, how do we know
is also algebraic?
We can try to repeatedly square the equation
. This gives us
. Then isolating the radical, we have
. Squaring again, we get
, so
is a root of
. This is in fact the unique monic polynomial of minimum degree that has
as a root (called the minimal polynomial of
) which shows
is algebraic. But a sum like
would probably have a minimal polynomial of much greater degree, and it would be much more complicated to construct this polynomial and verify the number is algebraic.
It is also increasingly difficult to construct polynomials for numbers like
. And, apparently there also exist algebraic numbers that cannot be expressed as radicals at all. This further complicates the problem.
In fact, the sum and product of algebraic numbers is algebraic – in fact, any number which can be obtained by adding, subtracting, multiplying and dividing algebraic numbers is algebraic. This means that a number like
![Rendered by QuickLaTeX.com \[\frac{\sqrt{2} + i\sqrt[3]{25} - 2 + \sqrt{3}}{15 - 3i + 4e^{5i\pi / 12}}\]](https://www.ocf.berkeley.edu/~rohanjoshi/wp-content/ql-cache/quicklatex.com-c53353269b5c7eb7cd6f8ad37a107c3b_l3.png)
is algebraic – there is some polynomial which has it as a root. But we will prove this result non-constructively; that is, we will prove that such a number must be algebraic, without providing an explicit process to actually obtain the polynomial. To establish this result, we will try to look for a deeper structure in the algebraic numbers, which is elucidated, perhaps surprisingly, using the tools of linear algebra.
Definition: Let
be a set of complex numbers that contains
. We say
is an abelian group if for all
,
and
. In other words,
is closed under addition and subtraction.
Some examples:
,
,
,
, the Gaussian integers
(i.e. the set of expressions
where
and
are integers)
Definition: An abelian group is a field if it contains
and for all
,
, and if
,
. In other words,
is closed under multiplication and division.
We generally use the letters
,
,
for arbitrary fields. Of the previous examples, only
,
,
are fields.
Exercise: Show that if
is a field,
.
Exercise: Show that the set
is a field. We call this field
. (Hint: rationalize the denominator).
Exercise: Describe the the smallest field which contains both
and
(this is called
)
Generally if
is a field and
some complex numbers, we will denote the smallest field that contains all of
and the elements
as
.
Definition: Let
be a field. A
-vector space is an abelian group
such that if
,
, then
. Intuitively, the elements of
are closed under scaling by
. (We also sometimes use the phrase “
is a vector space over
“)
Examples:
and
are both
-vector spaces. In fact,
is also a
vector space.
With this language in place, we can state the main goal of this post as follows:
Theorem: If
are algebraic numbers and
, then
is algebraic.
Note how this encompasses our previous claim that any number obtained by adding, subtracting, multiplying and dividing algebraic numbers is algebraic. For example, we can deduce the giant fraction above is algebraic by applying the theorem to
.
Definition: A field extension of a field
is just a field
that contains
. We will write this as “
is a field extension”.
Exercise: If
is a field extension, then
is a
-vector space
Consider a field like
. Not only is it a field, but it is also a
-vector space – its elements can be scaled by rational numbers. We want to adapt concepts from ordinary linear algebra to this setting. We want to think of
as a two dimensional vector space, with basis
and
– every element is can be uniquely written as a
-linear combination of
and
. Similarly, we would like to think of
as a
-vector space with basis
. Note that if we regard
as a
-vector space, its basis is
. This is because if an element is written as
, where the coefficients are rational, we can rewrite it as
, now with coefficients in
.
On the other hand, consider
. Since
is not algebraic, no linear combination (with rational coefficients) of the numbers
equals
without all the coefficients being zero. This makes them linearly independent – and makes
an infinite-dimensional vector space.
This finite-versus-infinite dimensionality difference will lie at the crux of our argument. Here is a rough summary of the argument to prove the Theorem:
- If we add an algebraic number to a field, we get a finite-dimensional vector space over that field.
- By repeatedly adding algebraic numbers to
, the resulting field will still be a finite-dimensional
-vector space. - Any element of a field which is a finite-dimensional
-vector space must be algebraic.
From here on, we will assume that the reader has some familiarity with ideas from linear algebra, such as the notions of linear combination, linear independence, and basis. We will only sketch proofs (maybe I will add details later). We would like to warn the reader that the proof sketches have many gaps and may be difficult to follow.
Definition: A field extension
is finite if there is a finite set of elements
such that every element of
can be represented as
, where all the
. We say the elements
generate (aka span)
over
. If, furthermore, every element can be represented uniquely in this form, then we say
is a
–basis (or just basis) for
.
Examples:
and
are finite extensions, while
is not.
Exercise: Show
is not a finite extension. (Hint:
is uncountable)
Proposition 1: Every finite field extension
has a basis.
Proof sketch: This is a special case of a famous theorem in linear algebra. Assume
generate
. Start with the last element. If
can be represented as a linear combination of
, remove it from the list. If we removed
, we now have
, and we can repeat the process with
. Continue this process until we cannot remove any more elements. Then (it can be checked that) we obtain a set whose elements are linearly independent. Then (it can be checked that) these form a
-basis for
. 
For the next theorem, keep in mind the example of the extensions
and
.
Proposition 2: Suppose
and
are finite field extensions. Then
is a finite extension.
Proof: By Proposition 1, both these field extensions have bases. Label the
-basis of
as
, and the
-basis for
as
. Then every element of
can be uniquely represented as
, for
. Furthermore, each
can be written uniquely as
for
. Plugging these in for the
shows that the
elements of the form
form a
-basis for
. 
Proposition 3: If
is algebraic, then
is a finite extension.
Proof sketch: Suppose
. First we will show every element in
is a polynomial in
with coefficients in
.
consists of all numbers that can be obtained by repeatedly adding, subtracting, multiplying and dividing
and elements of
. By performing some algebraic manipulations and combining fractions, we can see that every element can be written in the form
, where
and
are polynomials with coefficients in
.
Now I claim that for any polynomial
where
, there exists a polynomial
such that
. Let
be the minimal polynomial of
. Since
and
,
and
are relatively prime as polynomials. So there exist polynomials
and
such that
. Plugging in
into the polynomials gives us
. Since
,
, as desired.
Thus any element in
, written as
, can be written as
for an appropriate polynomial
; in other words, every element of
is a polynomial of
with coefficients in
. Now, suppose the minimal polynomial of
is
. Then
. So
(and all higher powers of
) can be written as rational linear combinations of
. Then it can be verified that every element in
can be written uniquely as a
-linear combination of
. This establishes that
is a finite extension; in particular, it has the basis

Proposition 4: Suppose
is a finite extension. Then any
is algebraic.
Proof sketch: Consider the elements
. They cannot all be linearly independent. This is because in a finite-dimensional vector space of dimension
any set with at least
elements must be linearly dependent. So there must be some linear combination of them which equals zero. But this simply means that for some constants
and positive integer
,
. Letting the
be the coefficients of a polynomial
,
, so
is algebraic. 
We have developed enough theory now to prove the result.
Proof of main Theorem: Let
be some collection of algebraic numbers. Then by Proposition 3,
are all finite field extensions. Applying Proposition 2 repeatedly gives us
is a finite extension. Then by Proposition 4, any element of
is algebraic.
This theorem essentially says that given a collection of algebraic numbers, by repeatedly adding, subtracting, multiplying and dividing them, we can only obtain algebraic numbers. But what about radicals? For example, we have now established that
is algebraic. Is
algebraic as well?
In fact, we can generalize our result to the following: any number obtained by repeatedly adding, subtracting, multiplying, dividing algebraic numbers, as well as taking
-th roots (for positive integers
) will be algebraic.
This is not too hard now that we have the main theorem. It suffices to show that if
is algebraic,
is algebraic. If
is algebraic, then there exist constants
such that
. This can be rewritten as
. Thus
is algebraic.
Acknowledgements: Thanks to Anton Cao and Nagaganesh Jaladanki for reviewing this article.