Faster individual discrete logarithms in non-prime finite fields with the NFS and FFS algorithms
Bibliographic record
Abstract
Computing discrete logarithms in finite fields is a main concern in cryptography. The best algorithms known are the Number Field Sieve and its variants in large and medium characteristic fields (e.g. GF$(p^2)$, GF$(p^{12})$); the Function Field Sieve and the Quasi Polynomial-time Algorithm in small characteristic finite fields (e.g. GF$(3^{6\cdot509})$). The last step of the NFS and FFS algorithms is the individual logarithm computation. It computes a smooth decomposition of a given target in two phases: an initial splitting then a descent tree. While new improvements have been made to reduce the complexity of the dominating relation collection and linear algebra steps of NFS and FFS, resulting in a smaller factor basis (database of known logarithms of small elements), the last step remains of same difficulty. Indeed, we have to find a smooth decomposition of a typically large element in the finite field. The method we propose improves the initial splitting phase and applies to any finite field of composite extension degree. It exploits the available subfields with a cheap (polynomial-time) linear algebra step, resulting in a much more smooth decomposition of the target. This leads to a new trade-off in the asymptotic complexity of the initial splitting step: for instance it is improved by a factor 2 in the exponent for FFS and 2 1/3 in the exponent for NFS, for any finite field of even extension degree, and with a much smaller smoothness bound. In medium and large characteristic, it can be combined with Pomerance's Early Abort strategy. In small characteristic, it replaces the Waterloo algorithm of Blake, Fuji-Hara, Mullin and Vanstone. Moreover it reduces the width and the height of the subsequent descent tree.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".