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ArticlePublished 7 Aug 20262 min readBy Kevin Jogininteger polynomial factorisationLLLrecombinationZassenhaus
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KEVOS AIFactoring Polynomials over the Integers

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Polynomial Factorisation

Factoring Polynomials over the Integers

The modular-lift-recombine pipeline, the exponential recombination problem, and the LLL-based algorithm that makes factorisation polynomial time.

Engineering / MathematicsPolynomial Factorisation2 min readKV-MATH-0569

Factoring a polynomial over the integers reduces to the finite field case plus lifting, but the reduction leaves a combinatorial problem behind. Removing that problem is the celebrated application of lattice reduction.

The classical pipeline

Squarefree→Factor mod p→Hensel lift→Recombine

Zassenhaus factorisation

  1. PreprocessRemove content and repeated factors.
  2. Choose a primeNot dividing the leading coefficient or the discriminant.
  3. Factor modulo pUsing the finite field pipeline.
  4. LiftBy Hensel lifting past the Mignotte bound.
  5. RecombineDetermine which products of modular factors are true integer factors.

The recombination problem

Caution

A polynomial irreducible over the integers may split into many factors modulo every prime. Testing all subsets is exponential in the number of modular factors, and there exist polynomials — the Swinnerton-Dyer family is the standard example — where this genuinely happens for every prime.

The classical algorithm is therefore exponential in the worst case despite every individual stage being polynomial. In practice it is usually fast, because random polynomials do not behave this way.

The LLL-based algorithm

Instead of searching combinations, construct a lattice in which a true factor corresponds to a short vector, then apply LLL.

Key point

The construction pairs the coefficients of a candidate factor against its p-adic approximation, scaled so that only genuine factors give short vectors. A true factor is short because its coefficients are bounded by Mignotte and it vanishes p-adically; everything else is long.

Integer polynomial factorisation algorithms
AlgorithmWorst caseTypical case
ZassenhausExponential in the modular factor countFast; usually few factors to recombine
LLL-basedPolynomialSlower constant factor
HybridPolynomialFast — try recombination briefly, fall back to LLL

Note

The hybrid is what production systems use. Attempt recombination with a bound on the subset size; if that fails, switch to the lattice method. This gives the typical speed of Zassenhaus with the worst-case guarantee of LLL.

Why this result mattered

Key point

The LLL paper established that polynomial factorisation over the rationals is polynomial time — a landmark result. The lattice algorithm was invented for this application, and its vastly wider usefulness became apparent afterwards.

Choosing the prime well

Trying several primes and keeping the one with the fewest modular factors materially reduces recombination work. Since the number of modular factors varies considerably between primes, this cheap heuristic is always worth applying.

Source. Henri Cohen, A Course in Computational Algebraic Number Theory, Springer GTM 138 — 3.5.2-3.5.4. Structural reference unverified: the source file was not available during authoring; chapter and section numbers are taken from the published edition and have not been checked against a physical copy.

Related pages

  • The LLL Lattice Basis Reduction Algorithm
  • Mignotte Bounds on Polynomial Factors
  • Hensel Lifting for Polynomial Factors
  • Factoring Polynomials over Algebraic Number Fields

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Hensel Lifting for Polynomial FactorsArticle · Engineering MathematicsNEXT LESSON →Factoring Polynomials over Algebraic Number FieldsArticle · Engineering MathematicsMignotte Bounds on Polynomial FactorsArticle · Engineering MathematicsRoot Finding over the Reals and Complex NumbersArticle · Engineering Mathematics
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