The Factor Theorem
Learning goals
- State the master identity
- Decide whether any linear expression, monic or not, is a factor of with one evaluation
- Turn one known root into a factor by division, then keep searching inside the smaller quotient
- Build a polynomial from prescribed real and nonreal roots plus one extra condition
- Explain why a nonzero polynomial of degree has at most roots
Roots and factors, two languages
The small example above is the pattern; here is the general rule behind it. Two definitions sit at the center of this chapter, and they live in different worlds. A number is a root (or zero) of the polynomial when . That is an arithmetic statement: plug in one number, get zero out. By contrast, a polynomial is a factor of when
exactly as is a factor of because with nothing left over. That is an algebraic statement about an identity that must hold for every value of at once. One definition asks about a single evaluation; the other asks about an exact division. There is no obvious reason these two ideas should have anything to do with each other. Yet the theorem of this lesson says that, for linear divisors, they are the very same thing.
The bridge between the two worlds was built in the last two lessons. Dividing by produces a quotient and a remainder,
where the remainder has degree smaller than the divisor , so is a constant. The remainder theorem then identifies which constant: substituting kills the first term and leaves . So for every polynomial and every number there is a single master identity,
and everything in this lesson is a consequence of reading it carefully.
Check your understanding
Dividing by gives a quotient and leaves remainder . What is ?
The master identity holds for every . Matching it to the given division forces : whatever the remainder is, that is the value .
Remainder would mean divides evenly, which is not what happened here. The quotient's value is computed from a different expression entirely and need not match the remainder.
The Factor Theorem
The Factor Theorem. Let be a polynomial and let be a number. Then is a factor of if and only if .
Take the phrase “if and only if” seriously: it is two separate claims bundled into one sentence. It claims that a root produces a factor, and it also claims that a factor produces a root. A statement of this shape is only proved when both directions are proved, so the proof below does them one at a time.
Proof of the Factor Theorem, both directions#
Divide by . As recalled above, the division algorithm and the remainder theorem together give the identity
valid for every , where is the quotient.
First suppose . The identity collapses to , which is precisely the definition of ” is a factor of ”: the division came out exact, and is the other factor. This is the direction that manufactures factorizations out of roots.
Now suppose instead that is a factor of , so that for some polynomial . A factorization is an identity, true for every value of , so it is in particular true at :
So is a root. Notice this direction did not even need the remainder theorem, only the freedom to evaluate an identity at the most convenient point. You can also see it through remainders: if the division by is exact, the remainder is , and the remainder theorem says the remainder is , so . Both directions are established, so the theorem holds as a genuine biconditional.
The two directions do different jobs, and you will use both constantly. Read left to right, a known factorization hands you roots for free: from you may write down and without computing anything. Read right to left, a known root hands you a factor, together with a genuine factorization whose quotient you can compute by synthetic or long division. And because the theorem is a biconditional, one evaluation settles the factor question in either direction: means is a factor, and means it is not. Nothing is left undecided.
One small generalization is worth recording now, because factors are not always monic. To test whether is a factor of , find the number that makes equal to : that is . Evaluate there. means is a factor; anything else means it is not. The rule of thumb covers every case: test the value that kills the candidate factor. For that value is , because .
The general version, for anyone who wants to see why it works: for ,
and multiplying a divisor by a nonzero constant never changes whether a division is exact: if , then , and is still a polynomial. So is a factor of exactly when , matching the shortcut above.
Check your understanding
Is a factor of ?
The factor vanishes at , so that is the value to test.
The factor theorem converts into the statement that is a factor. Testing would answer a different question, about the factor .
Check your understanding
Is a factor of ?
For the nonmonic factor , the value that kills it is , not and not .
So is a factor. Testing checks the factor instead, and testing checks instead; neither is the factor asked about here.
One root unlocks the whole factorization
Here is the workflow that makes the factor theorem the workhorse of this chapter. Suppose you know, or are told, a single root of a polynomial of degree . The theorem promises the factorization , and division delivers explicitly, a polynomial of degree . You have traded one problem for a strictly smaller one. Now hunt for a root of , peel again, and keep going until what remains is a quadratic, where factoring, the quadratic formula, and complex numbers finish the job completely.
Worked example 1 Factor a cubic from one known root
Factor completely, given that is a root.
First confirm the given root, since one evaluation is cheap insurance:
The factor theorem now guarantees . Find by synthetic division with on the coefficients :
The remainder in the corner re-confirms the root, and the other entries spell out the quotient . That quadratic factors by the Chapter 4 methods, since and multiply to and add to :
so the complete factorization is
and the roots of are , , and . Multiplying the three factors back together, or evaluating and directly, checks the answer.
Worked example 2 The quotient does not have to cooperate
Factor completely, given that is a root.
Confirm: . Peel off the factor by synthetic division on :
So . The quotient refuses to factor over the reals: its discriminant is
so it has no real roots, and over the real numbers the factorization above is already complete. But the previous chapter taught you exactly what a negative discriminant means. The quadratic formula runs to completion with and gives the conjugate pair
so over the complex numbers the cubic splits fully into linear factors:
The real root appears as a real linear factor; the nonreal pair hides inside the real quadratic factor until you allow complex factors. Both lines are correct answers, to two different questions: “factor over the reals” and “factor over the complex numbers”.
Check your understanding
The cubic has as a root. What is the quotient when it is divided by ?
Write the dividend with every power present, , so the synthetic row reads . Dividing by brings down , then , then , then .
The forgotten coefficient is the classic slip. The quotient factors further as , completing the factorization .
Worked example 3 Solve for an unknown coefficient
Find the value of so that is a factor of .
This runs the factor theorem backward from every example so far. Instead of a finished polynomial, has an unknown sitting inside it, and the factor condition is what pins it down. The theorem still says exactly the same thing: is a factor precisely when . So evaluate at , unknown and all:
Setting the factor condition turns the question into a linear equation for :
Check by substituting back: with , , and synthetic division by on leaves remainder , confirming the factor. The move to remember: an unknown coefficient inside does not change the rule at all. Evaluate at the number that kills the factor, set the result equal to , and solve the resulting equation for the unknown.
Check your understanding
For which value of is a factor of ?
The factor vanishes at , so the factor condition is .
Setting gives . The most common slip is a sign error on or ; keep the negative sign attached to each power separately.
Building a polynomial to order
The factor theorem also runs in reverse: instead of dissecting a given polynomial, you can manufacture one with any roots you choose. Each prescribed root contributes a factor , and the product of those factors has every prescribed number as a root. The reason is the easy direction of the theorem: evaluate the product at , and the factor makes the whole product zero.
A graph shows what you are really building. The real roots of a polynomial are exactly the -intercepts of its graph, because “the graph meets the axis at ” and "" are the same sentence. So a factored polynomial is a curve whose axis crossings you have pinned down in advance. The cubic below crosses cleanly through the axis at each root because each factor appears only once; a root can also touch the axis without crossing it when its factor repeats, which the next chapter takes up.
One caution before the examples: a list of roots does not pin down one polynomial. The cubics and have identical roots, and so does every other nonzero multiple. The construction really produces a whole family,
one polynomial for each choice of the leading coefficient , every one of them of degree .
Leaving the degree free is worse than just an open leading coefficient: a higher-degree polynomial can repeat one of the factors and still have the same distinct roots and pass through the same point. Compare with . Both have exactly the roots and , and both satisfy , yet has degree and has degree . So a problem that wants one specific polynomial has to do two things. It has to hold the degree at , which is what asking for a cubic with three roots, or for the least degree those roots allow, already does. And it has to supply one more condition to choose , such as the leading coefficient itself or a point the graph passes through. That point has to earn its keep. If its -value is one of the prescribed roots, there no matter what is, so it settles nothing. And its -value should not be at a new -value either: solving for would then force , which breaks the rule and means no polynomial of that exact degree with exactly those roots reaches that point. A usable extra point has an -value outside the root list and a nonzero -value.
Worked example 4 Prescribed roots plus one point
Find the cubic polynomial with roots , , and whose graph passes through .
The three roots force the shape for some nonzero constant , and the extra point pins down . Evaluate at :
Now expand step by step. First the last two factors:
then multiply by :
and finally scale by :
Check the point: . Check a root: . The monic cubic in the figure above is this polynomial’s sibling; the point is what selected from the whole family sharing those three crossings.
Check your understanding
For which value of does the quadratic pass through ?
Evaluate the family at the given point: .
The roots and fix the shape of the family; the point is the one extra condition that picks out from every nonzero multiple.
Worked example 5 Building with a nonreal root
Find the monic cubic with real coefficients that has and among its roots.
Two roots are given, but a cubic needs three linear factors, and the missing one comes for free. For a polynomial with real coefficients, a nonreal root always arrives with its conjugate: since is a root, must be one too. (The reason: conjugating both sides of the equation conjugates nothing but itself, because conjugation passes through sums and products and leaves real coefficients fixed. That argument, proved for quadratics in the complex numbers chapter, uses nothing about degree, so it applies here as well.)
Multiply the conjugate pair of factors first, because that product is guaranteed to come out real:
and since , this is . Now attach the real root’s factor:
Every coefficient is real, as promised. The pattern is worth remembering: a real root shows up as a real linear factor. A nonreal root and its conjugate instead merge into a real quadratic factor, here , whose discriminant is negative. The next chapter builds its whole theory of real polynomials on this pairing.
Check your understanding
A monic cubic with real coefficients has and among its roots. What is its factorization over the reals?
Real coefficients force the conjugate to be a root as well, so the three factors are , , and . The conjugate pair multiplies into a real quadratic.
Over the reals the factorization stops at , since has no real roots. Note the sign trap: the root gives the factor , not .
A ceiling on the number of roots
The factor theorem’s first big structural consequence is a hard limit. A quadratic never has three roots, a cubic never has four, and the pattern is completely general.
Here is why, in one small case. Take a cubic, degree , and suppose it has three different roots. Each root peels off one linear factor, and each peel drops the degree by exactly : start at degree , peel once to reach degree , peel again to reach degree , peel a third time to reach degree , a nonzero constant with no left in it to make it vanish anywhere. There is no degree left to peel a fourth time, so a fourth distinct root is impossible, unless the cubic was the zero polynomial to begin with. The general argument below just runs this same budget for any degree .
A nonzero polynomial of degree has at most roots#
Let be a nonzero polynomial of degree , and let be distinct roots of it. Because is a root, the factor theorem writes
and comparing degrees on the two sides, has degree . Now evaluate this identity at the second root:
The first factor is not zero, because the roots are distinct, and a product of two numbers is zero only when one of them is zero. So , and the factor theorem applies again, this time to : we get with of degree , hence
Repeat the same evaluation argument with , each time inside the newest quotient. Every distinct root peels off one more linear factor, and every peel lowers the quotient’s degree by exactly . After all roots are used,
where is not the zero polynomial (otherwise would be) and has degree . A degree is never negative, so , which is the claim: .
Read the theorem for what it is: a ceiling, not a promise. It says a degree- polynomial cannot have more than roots; it is silent about how many actually exist. The quadratic has no real roots at all, and no theorem in this lesson objects, because zero is comfortably at most two. The factor theorem converts roots into factors and factors into roots, but it never manufactures a root out of nothing. Two questions stay open here: whether roots must exist at all, and how many a degree- polynomial is guaranteed once complex numbers are allowed. Both are the business of the Fundamental Theorem of Algebra, the headline of the next chapter.
The ceiling still has real bite. If someone hands you a cubic and four distinct numbers where it vanishes, you may conclude the “cubic” is actually the zero polynomial, no computation required. And in Worked Example 1, once the roots , , and were in hand, the search was legitimately over: a cubic has no room for a fourth root.
Check your understanding
A polynomial is claimed to have degree at most , and it is also known to vanish at the five distinct numbers . What must be true?
A nonzero polynomial of degree at most has at most roots. Five distinct roots is too many for any nonzero polynomial of that degree, so the only polynomial of degree at most that can vanish at all five numbers is the zero polynomial, for every .
The ceiling is not about what degree the polynomial "really" is; the degree was fixed at or below by the claim. Too many roots for that ceiling forces the polynomial itself to be zero, not the ceiling to bend.
There is one honest gap left in the workflow, and you should notice it. Everything above starts from one known root, and in this lesson the root was always handed to you. Where does the first root come from when nobody supplies it? Testing numbers blindly could take forever. The next lesson closes exactly this gap: for polynomials with integer coefficients, the Rational Root Theorem shrinks the infinite search to a short finite list of rational candidates. The factor theorem then processes each of those candidates with a single evaluation.