Editing Integer factorization (section)

{{Short description|Decomposition of a number into a product}}
{{Redirect|Prime decomposition|the prime decomposition theorem for 3-manifolds|Prime decomposition of 3-manifolds}}
{{unsolved|computer science|Can integer factorization be solved in polynomial time on a classical computer?}}

In [[mathematics]], '''integer factorization''' is the decomposition of a [[positive integer]] into a [[Product (mathematics)|product]] of integers. Every positive integer greater than 1 is either the product of two or more integer [[divisor|factors]] greater than 1, in which case it is a [[composite number]], or it is not, in which case it is a [[prime number]]. For example, {{math|15}} is a composite number because {{math|1=15 = 3&thinsp;·&thinsp;5}}, but {{math|7}} is a prime number because it cannot be decomposed in this way. If one of the factors is composite, it can in turn be written as a product of smaller factors, for example {{math|1=60 = 3&thinsp;·&thinsp;20 = 3&thinsp;·&thinsp;(5&thinsp;·&thinsp;4)}}. Continuing this process until every factor is prime is called '''prime factorization'''; the result is always unique up to the order of the factors by the [[prime factorization theorem]].

To factorize a small integer {{mvar|n}} using mental or pen-and-paper arithmetic, the simplest method is [[trial division]]: checking if the number is divisible by prime numbers {{math|2}}, {{math|3}}, {{math|5}}, and so on, up to the [[square root]] of {{mvar|n}}. For larger numbers, especially when using a computer, various more sophisticated factorization algorithms are more efficient. A prime factorization algorithm typically involves [[primality test|testing whether each factor is prime]] each time a factor is found.

When the numbers are sufficiently large, no efficient non-[[quantum computer|quantum]] integer factorization [[algorithm]] is known. However, it has not been proven that such an algorithm does not exist. The presumed [[Computational hardness assumption|difficulty]] of this problem is important for the algorithms used in [[cryptography]] such as [[RSA (cryptosystem)|RSA public-key encryption]] and the [[Digital Signature Algorithm|RSA digital signature]].<ref>{{Citation |last=Lenstra |first=Arjen K. |title=Integer Factoring |date=2011 |url=http://link.springer.com/10.1007/978-1-4419-5906-5_455 |encyclopedia=Encyclopedia of Cryptography and Security |pages=611–618 |editor-last=van Tilborg |editor-first=Henk C. A. |place=Boston, MA |publisher=Springer US |language=en |doi=10.1007/978-1-4419-5906-5_455 |isbn=978-1-4419-5905-8 |access-date=2022-06-22 |editor2-last=Jajodia |editor2-first=Sushil}}</ref> Many areas of [[mathematics]] and [[computer science]] have been brought to bear on this problem, including [[elliptic curve]]s, [[algebraic number theory]], and quantum computing.

Not all numbers of a given length are equally hard to factor. The hardest instances of these problems (for currently known techniques) are [[semiprime]]s, the product of two prime numbers. When they are both large, for instance more than two thousand [[bit]]s long, randomly chosen, and about the same size (but not too close, for example, to avoid efficient factorization by [[Fermat's factorization method]]), even the fastest prime factorization algorithms on the fastest classical computers can take enough time to make the search impractical; that is, as the number of digits of the integer being factored increases, the number of operations required to perform the factorization on any classical computer increases drastically.

Many cryptographic protocols are based on the presumed difficulty of factoring large composite integers or a related problem {{Ndash}}for example, the [[RSA problem]]. An algorithm that efficiently factors an arbitrary integer would render [[RSA (algorithm)|RSA]]-based [[public-key]] cryptography insecure.