Algebraic numbers is algebraically closed, proved using elementary linear algebra
Recall the definition of algebraic numbers:
Definition of Algebraic Numbers
Let
be a complex number. We call it an algebraic number iff , , s.t. .
Algebraic number is algebraically closed
The algebraic numbers have an intriguing property: it is algebraically closed. That is, the sum, difference, product and quotient of two algebraic numbers is again algebraic. Moreover, the roots of a non-zero polynomial whose coefficients are algebraic numbers is again algebraic.
Theorem 1
Let
be some algebraic number, where . Then , , are all algebraic numbers.
Theorem 2
Let
be some algebraic numbers, where and holds. Then all the roots of the polynomial are algebraic numbers.
Trivial as it sounds, the two theorems might not so easy to prove unless you are familiar with field theory. (You can pause now and try to prove them yourself!) However, with some matrix tricks, we can prove these theorems with just elementary linear algebra.
From Polynomials to Matrices
Here we introduce our main tool for proving the two theorems: the companion matrix.
Definition of Companion Matrix
For
, let be a polynomial. We define the companion matrix of to be:
One can verify that the characteristic polynomial of
Lemma 1
Let
be a complex number. Then is an algebraic number iff is the eigenvalue of some matrix with rational entries.
Proof of Lemma 1
For the if part, obviously the the coefficients of the
characteristic polynomial of a matrix with rational entries are
rational. Thus
For the only if part, let
Proof of the Theorems
With the tool of matrix in hand, we can now easily prove Theorem 1:
Proof of Theorem 1
By Lemma 1, there exists some rational
matrices
Similarly, we have
To show that
Proof of Theorem 2
We still use matrices as our main weapon, however, the proof is a
little more involved. Let
Let
Summary
This elegant proof transforms the roots of polynomials into eigenvalues of matrices, and use tensor products to manipulate them freely. An alternate proof contains constructions with resultant, however, the process is more tedious and less intuitive, and requires some knowledge in abstract algebra.