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ArticlePublished 7 Aug 20262 min readBy Kevin JoginMPQSmultiple polynomialsself initialisingSIQS
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Modern Factoring Methods

The Multiple Polynomial Quadratic Sieve

MPQS: using many polynomials with short intervals to keep values small, and the self-initialising variant.

Engineering / MathematicsModern Factoring Methods2 min readKV-MATH-0674

The single-polynomial sieve suffers from values that grow as the interval extends. Using many polynomials, each sieved over a short interval, keeps every value small and is the decisive improvement.

The problem

Caution

Values of the single polynomial grow roughly linearly with distance from the centre. Far out, they are large enough that smoothness becomes rare and the sieving is nearly worthless.

The fix

Use a family of quadratic polynomials, each with a short interval around its own centre where values stay small. Switch polynomials rather than extending any one interval.

Q(x) = A x^2 + 2 B x + C, with B^2 - A C = nChosen so values stay small across a short interval.

MPQS polynomial generation

  1. Choose the leading coefficientAs a product of factor base primes, sized so values remain small.
  2. Solve for the middle coefficientBy square roots modulo each prime factor, combined by the Chinese remainder theorem.
  3. Derive the constantFrom the discriminant condition.
  4. Compute sieve rootsFor each factor base prime under the new polynomial.
  5. Sieve the short intervalThen move to the next polynomial.

The initialisation cost

Pitfall

Each new polynomial requires recomputing the sieve starting positions for every factor base prime. With a base of millions of primes and short intervals, this initialisation can dominate the sieving itself.

Self-initialisation

SIQS chooses the leading coefficient as a product of several primes, then generates many polynomials sharing it by varying signs in the Chinese remainder reconstruction. Sieve roots for the family are obtained by cheap updates rather than full recomputation.

Key point

Self-initialisation amortises the setup across a whole family of polynomials. It is what makes short intervals affordable, and SIQS is the standard form of the quadratic sieve in practice.

Quadratic sieve variants
VariantValue sizeInitialisation cost
Single polynomialGrows across a long intervalOnce
MPQSSmall throughoutPer polynomial — significant
SIQSSmall throughoutAmortised across a family

Choosing the interval length

Cost

Shorter intervals keep values smaller but increase the number of polynomials and hence the initialisation burden. The optimum is found by measurement and depends heavily on cache size, since the interval should fit in cache.

Where the sieve stands

SIQS is the method of choice for targets up to roughly a hundred digits. Beyond that the number field sieve is faster — see the number field sieve and method comparison.

Source. Henri Cohen, A Course in Computational Algebraic Number Theory, Springer GTM 138 — 10.4.2-10.4.3. 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

  • Modern Factoring Methods Compared
  • The Quadratic Sieve: Sieving Stage
  • The Quadratic Sieve: Linear Algebra Stage

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The Quadratic Sieve: Sieving StageArticle · Engineering MathematicsNEXT LESSON →The Quadratic Sieve: Linear Algebra StageArticle · Engineering MathematicsQuadratic Sieve Factor Base SelectionArticle · Engineering MathematicsNumber Field Sieve: Polynomial Selection and StructureArticle · Engineering Mathematics
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