Complex polynomials
Complex coefficients, roots, factorisation and real factors.
This page studies polynomials whose coefficients come from complex arithmetic. It asks which polynomials have roots in and how those roots determine the factors. The central result is that every non-constant complex polynomial splits into linear factors.
Polynomial language
Complex number
A complex number is a number of the form
The expression is the Cartesian form of . Its real part is and its imaginary part is .
Complex polynomial
A complex polynomial is a function of the form
for a non-negative integer .
The coefficients may be real or non-real. The word polynomial restricts the exponents of the variable to non-negative integers, not the kinds of numbers allowed as coefficients.
Degree
For a non-zero polynomial
the degree of is the largest integer for which . The zero polynomial has undefined degree.
Classify and find degrees
The expressions
are complex polynomials. The non-zero polynomials , , and have degrees , , and , respectively. The zero polynomial has undefined degree.
An expression containing a fractional power such as is not a polynomial in . By contrast, numbers such as and are allowed as coefficients: only the powers of the variable must be non-negative integers.
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- [1]:Complex polynomial
- [2]:Degree
Polynomial root
A number is a root of if
Polynomial factor
A polynomial is a factor of if there is a polynomial such that
for every .
Find and use roots
For ,
so is a root. Direct substitution also gives . The factor theorem below explains why these roots yield the factorisation
into linear factors. The same polynomial has no real root: for real , .
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- [1]:Polynomial root
- [2]:Polynomial factor
Root multiplicity
For a non-zero polynomial , a root has multiplicity if is a factor of but is not.
- [1]:Polynomial root
- [2]:Polynomial factor
- [3]:Multiplicity two
Multiplicity two
For , the root has multiplicity two: is a factor of but is not.
Multiplicity records repetition in a factorisation. It is not the number of different solutions: this polynomial has one distinct root, counted twice when roots are counted with multiplicity.
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Roots and factors
Remainder theorem
When is divided by , the remainder is the complex number .
- [1]:Polynomial root
- [2]:Proof
- [3]:Factor theorem
Polynomial division gives
with a constant remainder . At , the first term vanishes, leaving .
Factor theorem
A number is a root of if and only if is a factor of .
If , polynomial division gives
so is a factor. Conversely, if , then .
- [1]:Factor theorem
Uniqueness of polynomial coefficients
Two complex polynomials agree for every if and only if their corresponding coefficients are equal. In particular, a complex polynomial is zero for every if and only if all its coefficients are zero.
This is why identities between polynomial functions can be checked coefficient by coefficient.
To see why, subtract the two polynomials. If the result were a non-zero polynomial of degree , it could have at most distinct roots: each root supplies a linear factor by the factor theorem. After dividing out , any other root remains a root of the quotient, so we can repeat this step at most times. It therefore cannot vanish at every complex number.
- [1]:Complex polynomial
- [2]:Factor theorem
Fundamental theorem of algebra
Every complex polynomial of degree has at least one complex root.
The theorem is stated without proof. It guarantees that a root exists, not a formula for finding it.
Applying the factor theorem to that root and then repeating with the quotient, gives the full factorisation below. This is what is special about : over , a polynomial such as cannot be factored into real linear factors.
Factorisation over
Factorisation over the complex numbers
Every complex polynomial of degree can be written
where is the coefficient of and the are roots, counted with multiplicity.
Proceed by induction on the degree. A degree-one polynomial already has the required form. Suppose the result holds for degree . If has degree , the fundamental theorem of algebra supplies a root . The factor theorem gives
for a polynomial of degree . Apply the induction hypothesis to and multiply the factors.
Count roots with multiplicity
A complex polynomial of degree has exactly complex roots counted with multiplicity.
The factorisation theorem gives linear factors. Conversely, if , then
so equals one of the . Thus the list has no missing roots, and repeated factors record multiplicity.
“Exactly roots” does not mean distinct values: has two roots counted with multiplicity, but only one distinct root.
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Returning to real factors
A real polynomial is one whose coefficients all lie in , even when the variable takes complex values. Its non-real roots occur in conjugate pairs, so its complex factorisation can be regrouped into real factors.
Conjugate roots for real coefficients
If has real coefficients and is a root of multiplicity , then is also a root of multiplicity .
The condition on the coefficients is essential: conjugation must leave every coefficient unchanged before it can pass through the polynomial.
Write with . If , then
For multiplicity, write with . Since has real coefficients, conjugating this factorisation gives , where and . Thus the conjugate root has exactly the same multiplicity.
Real linear and quadratic factors
Every non-zero polynomial with real coefficients can be written as a non-zero real constant times a product of real linear factors and real quadratic factors.
Every non-real root occurs with its conjugate. Pairing the corresponding factors gives
whose coefficients are real. Real roots already provide real linear factors. The quadratic factors obtained from non-real roots have negative discriminant, so they do not split further over .
Factorise over the reals
The real roots are and . Pairing the remaining eighth roots with their conjugates gives
The complex factorisation and the real factorisation describe the same polynomial; the latter groups conjugate roots into quadratic factors.
[exam]
The characteristic equation of a linear recurrence or differential equation is a complex polynomial. Stability of systems reads growth, decay and oscillation from its roots; the continuous case is the second-order constant-coefficient method from Calculus.