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GuidePublished 7 Aug 2026Updated 13 Aug 20267 min readBy Kevin Joginfactor basesmoothnessbound selectiontrade-off
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Sub-exponential Class Group Computation

Factor Base Selection and Smoothness

Choosing the factor base for class group computation, the smoothness trade-off, and how base size interacts with the linear algebra.

Engineering / MathematicsSub-exponential Class Group Computation8 min readKV-MATH-0628

The factor base is the set of prime ideals over which relations are expressed. Its size controls a trade-off that determines the entire running time of the algorithm.

The trade-off

The factor base size trade-off
Larger factor baseSmaller factor base
Relations easier to findRelations harder to find
More relations neededFewer relations needed
Larger matrix; harder linear algebraSmaller matrix
Higher smoothness probabilityLower smoothness probability

Key point

The optimum balances relation collection time against linear algebra time. Because these scale differently, the balance point moves with the discriminant, and a base size tuned for one problem size is wrong for another.

Construction

Building the factor base

  1. Choose a norm boundGuided by the theory and by measurement.
  2. Enumerate rational primesUp to the bound.
  3. Decompose eachFind the primes above it — see simple decomposition.
  4. Filter by normKeep prime ideals whose norm is below the bound; inert primes of high residue degree are usually excluded.
  5. IndexStore in a structure supporting fast smoothness testing.

Note

Prime ideals of residue degree greater than one have large norm and rarely appear in smooth relations, so they are often excluded. Including them enlarges the matrix for little gain.

The generation guarantee

Under GRH, prime ideals of norm below a bound proportional to the square of the logarithm of the discriminant generate the class group. This is what makes a small factor base sufficient — see Minkowski and Bach bounds.

Caution

If the base fails to generate, the computed group is a quotient of the true class group. This failure is invisible in the relation matrix and is caught only by external verification.

Smoothness

An ideal is smooth over the base when its norm factors entirely over the corresponding rational primes and every prime ideal appearing is in the base.

Pitfall

Norm smoothness is necessary but not sufficient. A norm may factor over the base primes while the ideal itself involves a prime above one of them that was excluded from the base. Both conditions must be checked.

Testing smoothness

Cost

Smoothness testing is the inner loop and dominates relation collection. Trial division against the base primes is usually the right method because the base is exactly the set of primes of interest; batch methods amortise across many candidates.

Practical sizing

In practice the base is sized by measurement rather than by the theoretical optimum: run a short relation collection at several base sizes and extrapolate. The theoretical bound then serves as a completeness guarantee rather than as the operative sizing rule.

Source. Henri Cohen, A Course in Computational Algebraic Number Theory, Springer GTM 138 — 6.5.2. 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

  • Minkowski and Bach Bounds
  • Smoothness and Sub-exponential Complexity
  • Quadratic Sieve Factor Base Selection
  • Ideal Reduction in Number Fields
  • Relation Matrix Construction

Handbook application: from concept to controlled practice

Purpose. This expanded section turns the original page into a practical handbook. It preserves the supplied material and adds a repeatable way to apply, check and review Factor Base Selection and Smoothness. It does not replace a contract, legislation, a controlled standard, competent engineering judgement or specialist advice.

The operating aim is to turn a compact mathematical statement into a usable chain of definitions, claims, examples and checks. Read the original explanation first, then use the workflow and checks below to convert knowledge into evidence.

Treat Factor Base Selection and Smoothness as a network of definitions and implications, not as a list of formulas. The working vocabulary on this page—smoothness, base, factor, trade-off, selection—should be made explicit before any proof or computation begins. Record the ambient set or structure, the permitted operations and the equality or equivalence relation in use. A compact theorem often changes meaning when the base field, finiteness condition, commutativity assumption or direction of an action changes.

For a proof, write the hypotheses as a checklist and mark the line at which each one is used. For a computation, state the representation of the input, the arithmetic model, the termination condition and the output invariant. For a classification problem, distinguish existence from uniqueness and distinguish an object from its representation. These separations prevent a correct local calculation from being mistaken for the general result.

A useful worked example should be small enough to inspect completely but rich enough to exercise the main mechanism. Compute the result in two ways where practical: symbolically and by substitution, structurally and numerically, or directly and through a normal form. Then include one near-miss example in which a hypothesis fails. The contrast explains why the theorem is shaped as it is and gives the reader a diagnostic pattern for later problems.

Verification is part of the mathematics. Check domains and codomains, substitute proposed solutions, test identity and zero cases, compare dimensions or cardinalities, and confirm that maps respect the required operations. In numerical work, report precision, conditioning and a residual rather than digits alone. In algorithmic work, separate mathematical correctness from implementation complexity and resource limits.

Step-by-step operating method

  1. Fix the setting. State the objects, ambient structure, notation and assumptions before manipulating symbols.
  2. Separate claims. Distinguish definitions, hypotheses, conclusions, equivalent conditions and consequences.
  3. Choose a method. Select proof, construction, calculation or algorithm according to the question actually asked.
  4. Work a small case. Use the smallest non-trivial example to expose the mechanism and test edge behaviour.
  5. Verify independently. Substitute back, check invariants, test boundary cases or use an alternative derivation.

Worked-example protocol

Illustrative method—not a source theorem. Start with a small admissible input and list the definitions it must satisfy. Carry out each transformation on a separate line, citing the property that permits it. Preserve exact values until approximation is necessary. At the end, verify the output against the original definition and one invariant such as dimension, degree, determinant, order, norm or residual. Then alter one hypothesis and observe which step ceases to be valid. This protocol creates a reusable example without inventing a theorem-specific numerical answer.

StageRecordQuality check
InputObjects, domain, notation, assumptionsEvery symbol is defined
MethodPermitted operation or cited result at each stepAll hypotheses hold
OutputExact result and representationCorrect type, domain and form
VerificationSubstitution, invariant or alternative derivationIndependent agreement
Boundary testZero, identity, degenerate or failed hypothesisScope is understood

Common failure modes and recovery actions

1. Watch for

Using a theorem without checking every hypothesis.

Recovery: Return to the governing definition or requirement and restate the decision in one sentence.

2. Watch for

Treating a suggestive example as a proof of the general case.

Recovery: Separate evidence from assumption, assign an owner and set a date for validation.

3. Watch for

Changing notation or conventions part-way through an argument.

Recovery: Run a small counterexample, boundary test, pilot or independent check before proceeding.

4. Watch for

Hiding a division-by-zero, convergence, finiteness or commutativity assumption.

Recovery: Record the consequence, decision and rationale, then update the controlled baseline.

5. Watch for

Reporting a computed result without a residual, substitution or structural check.

Recovery: Escalate when the issue affects safety, compliance, acceptance, material value or an agreed tolerance.

Review checklist

  • Can every symbol be traced to a definition or prior result?
  • Which hypothesis does each major step use?
  • Does the method cover zero, identity, degenerate and boundary cases?
  • Can the conclusion be checked by a second representation or calculation?
  • Are mandatory requirements distinguished from recommendations and illustrative values?
  • Are sources, assumptions, units, dates and versions recorded closely enough to reproduce the decision?
  • Have safety, legal, ethical, stakeholder and operational consequences been considered at the appropriate level?
  • Is there a named owner and a trigger for review, escalation, change or retirement?

Questions for deeper application

What is the most important distinction a practitioner must preserve when applying Factor Base Selection and Smoothness?

Answer with a fact or cited source where available. Where evidence is incomplete, record the assumption, consequence, responsible owner and next validation action.

Which assumption about smoothness would change the result most if it proved false?

Answer with a fact or cited source where available. Where evidence is incomplete, record the assumption, consequence, responsible owner and next validation action.

What evidence would allow an independent reviewer to reproduce or challenge the conclusion?

Answer with a fact or cited source where available. Where evidence is incomplete, record the assumption, consequence, responsible owner and next validation action.

Which boundary, exception or failure case has not yet been tested?

Answer with a fact or cited source where available. Where evidence is incomplete, record the assumption, consequence, responsible owner and next validation action.

What must be handed over, monitored or reviewed after the immediate work is complete?

Answer with a fact or cited source where available. Where evidence is incomplete, record the assumption, consequence, responsible owner and next validation action.

Authoritative references and use notes

The sources below were selected as institutional or primary guidance for the broader practice. They support the handbook method; they do not imply that every statement or clause in a source applies to every project. Confirm the current edition, jurisdiction, contract and application before treating any requirement as mandatory.

  • MIT OpenCourseWare — Number Theory I — Massachusetts Institute of Technology. Used for algebraic and analytic number theory. Accessed 2026-08-13.
  • MIT OpenCourseWare — Algebra I — Massachusetts Institute of Technology. Used for groups, vector spaces, linear transformations and linear groups. Accessed 2026-08-13.

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