A Brief History of Factoring and Primality Testing B. C. (Before Computers)
Notice bibliographique
Résumé
Factoring and primality testing have become increasingly important in today's information based society, since they both have produced techniques used in the secure transmission of data. However, often lost in the modern-day shuffle of information are the contributions of the pioneers whose ideas ushered in the computer age and, as we shall see, some of whose ideas are still used today as the underpinnings of powerful algorithms for factoring and primality testing. We offer this brief history to help readers know more about these contributions and appreciate their significance. Virtually everyone who has graduated from high school knows the definition of a number, namely a p E N = {1, 2, 3, 4, . . .} such that p > 1 and if p = Em where X, m E , then either f = 1 or m = 1. (If E N and > 1 is not prime, then is called composite.) Although we cannot be certain, the concept of primality probably arose with the ancient Greeks over two and one-half millennia ago. The first recorded definition of numbers was given by Euclid around 300 BCE in his Elements. However, there is some indirect evidence that the concept of primality might have been known far earlier, for instance, to Pythagoras and his followers. The Greeks of antiquity used the term arithmetic to mean what today we would call number theory, namely the study of the properties of the natural numbers and the relationships between them. The Greeks reserved the word logistics for the study of ordinary computations using the standard operations of addition/subtraction and multiplication/division, which we now call arithmetic. The Pythagoreans introduced the term mathematics, which to them meant the study of arithmetic, astronomy, geometry, and music. This curriculum became known as the quadrivium in the Middle Ages. Although we have enjoyed the notion of a for millennia, only very recently have we developed eJficient tests for primality. This seemingly trivial task is in fact much more difficult than it appears. A primality test is an algorithm (a methodology following a set of rules to achieve a goal), the steps of which verify that given some integer n, we may conclude n is a number. A primality proof is a successful application of a primality test. Such tests are typically called true primality tests to distinguish them from probabilistic primality tests (which can only conclude that n is prime up to a specified likelihood). We will not discuss such algorithms here (see [9] for these). A concept used frequently in primality testing is the notion of a sieve. A is a process to find numbers with particular characteristics (for instance primes) by searching among all integers up to a prescribed bound, and eliminating invalid candidates until only the desired numbers remain. Eratosthenes (ca. 284-204 scE) proposed the first sieve for finding primes. The following example illustrates the Sieve of Eratosthenes.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».