Basic Aim
The algorithm attempts to set up a congruence of squares modulo n (the integer to be factorized), which often leads to a factorization of n. The algorithm works in two phases: the data collection phase, where it collects information that may lead to a congruence of squares; and the data processing phase, where it puts all the data it has collected into a matrix and solves it to obtain a congruence of squares. The data collection phase can be easily parallelized to many processors, but the data processing phase requires large amounts of memory, and is difficult to parallelize efficiently over many nodes or if the processing nodes do not each have enough memory to store the whole matrix. The block Wiedemann algorithm can be used in the case of a few systems each capable of holding the matrix.
The naive approach to finding a congruence of squares is to pick a random number, square it, and hope the least non-negative remainder modulo n is a perfect square (in the integers). For example, 802 mod 5959 is 441, which is 212. This approach finds a congruence of squares only rarely for large n, but when it does find one, more often than not, the congruence is nontrivial and the factorization is complete. This is roughly the basis of Fermat's factorization method.
The quadratic sieve is a modification of Dixon's factorization method.
The general running time required for the quadratic sieve (to factor an integer n) is
in the L-notation.
The constant e is usually used as the base of the logarithm.
Read more about this topic: Quadratic Sieve
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