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Factorization workspace

Numerisect 0.5.0 keeps factorization in native engines while Python manages validation, processes, persistence, cancellation, and result verification.

Routing and manual strategies

Automatic mode sends inputs below the configured decimal threshold to YAFU. At or above the threshold it runs a YAFU pretest and sends a sufficiently large residual to the nearest suitable installed CADO parameter set. Direct YAFU, Msieve, CADO, and hybrid modes remain available.

The manual laboratory exposes bounded PARI/GP trial division and YAFU Pollard rho/Brent, Pollard p−1, Williams p+1, ECM, SIQS, and NFS entry points. The trial bound is recorded with the job. SQUFOF is available as its own backend, served by the project's compiled numerisect-squfof helper because no installed library exposes a standalone SQUFOF entry point. See the expert factorization laboratory.

Engine availability does not guarantee that every algorithm is appropriate for every input or that a particular third-party build is defect-free. A nonzero exit, crash, malformed factor line, nondividing factor, or incomplete factor product fails explicitly and preserves the log.

Trees, partial results, and batches

The result pane groups repeated factors into exponent leaves and displays status, decimal length, discovery engine, and total elapsed time. Composite or unknown leaves can be continued as linked child jobs. The parent result remains unchanged and the child records its parent job ID.

Batch mode accepts newline text, CSV-like values, or a JSON array. Every expression is validated before any job is queued. The existing controlled worker count prevents a file import from bypassing CPU concurrency limits. A consolidated report becomes available only after every selected job reaches a terminal state.

Verification and provenance

Every factor must divide the requested input, and the complete returned multiset must multiply to its absolute value. Cross-check mode runs YAFU and Msieve independently and accepts the result only when their sorted factor multisets are identical.

Successful jobs save both:

  • output/factorization-<job>.txt, the human-readable equation and provenance;
  • output/factorization-<job>.json, a reproducibility manifest.

The JSON schema identifier is org.numerisect.factorization-manifest.v1. It records input/equation hashes, factors and proof labels, parent ID, requested and selected strategy, CPU/pretest/trial/CADO parameters, complete native command history, configured immutable engine revisions, and SHA-256 hashes of the executables actually resolved on the host. A configured revision describes Numerisect's reviewed source pin; the executable hash distinguishes a different pre-existing system build.

Campaign features

These were once deferred and have since shipped. Each is documented on its own page:

  • SQUFOF, expert B1/B2/sigma scheduling, symbolic SNFS and Aurifeuillean analysis, the strategy adviser and algorithm traces are in the expert factorization laboratory.
  • Resumable GMP-ECM campaigns use the engine's own save and resume residue files; see the same page.
  • Distributed CADO-NFS across trusted workers is in Distributed CADO-NFS. Read its trust model before enabling it: CADO authenticates clients by IP address only.

Separate CADO stage control remains partial: parameters are exposed and stage progress is parsed from the log, but running an individual stage in isolation is not implemented. See Roadmap status.