The number of relations in the quadratic sieve algorithm

The subject of our study is the single large prime variation of the quadratic sieve algorithm. We derive a formula for the average numbers of complete and incomplete relations per polynomial, directly generated by the algorithm. The number of additional complete relations from the incomplete relations is then computed by a known formula. Hence practical hints for the optimal choice of the parameter values can be derived. We further compare theoretical estimates for the total number of smooth integers in an interval with countings in practice

The Number of Relations in the Quadratic Sieve Algorithm

The subject of our study is the single large prime variation of the quadratic sieve algorithm. We derive a formula for the average numbers of complete and incomplete relations per polynomial, directly generated by the algorithm. The number of additional complete relations from the incomplete relations is then computed by a known formula. Hence practical hints for the optimal choice of the parameter values can be derived. We further compare theoretical estimates for the total number of smooth integers in an interval with countings in practice. AMS Subject Classification (1991): 11A51, 11Y05 CR Subject Classification (1991): F.2.1 Keywords & Phrases: Factorization, Multiple Polynomial Quadratic Sieve, Vector supercomputer, Cluster of work stations 1. Introduction We assume that the reader is familiar with the multiple polynomial quadratic sieve algorithm [Bre89, Pom85, PST88, Sil87, RLW89]. We consider the single large prime variation of the algorithm and write MPQS for short. If we ..

Factoring integers with large-prime variations of the quadratic sieve

We present the results of many factorization runs with the single and double large prime variations (PMPQS, and PPMPQS, respectively) of the quadratic sieve factorization method on SGI workstations, and on a Cray C90 vectorcomputer. Experiments with 71--, 87--, and 99--digit numbers show that for our Cray C90 implementations PPMPQS beats PMPQS for numbers of more than 80 digits, and this cross--over point goes down with the amount of available central memory. For PMPQS a known theoretical formula is worked out and tested that helps to predict the total running time on the basis of a short test run. The accuracy of the prediction is within 10% of the actual running time. For PPMPQS such a prediction formula is not known and the determination of an optimal choice of the parameters for a given number would require many full runs with that given number, and the use of an inadmissible amount of CPU--time. In order yet to provide measurements that can help to determine a good choic..

Factorizations of a^n ± 1, 13 ≤ a < 100: Update 2

This Report updates the tables of factorizations of a n \Sigma 1 for 13 a ! 100, previously published as CWI Report NM-R9212 (June 1992) and updated in CWI Report NM-R9419 (September 1994). A total of 760 new entries in the tables are given here. The factorizations are now complete for n ! 67, and there are no composite cofactors smaller than 10 94 . 1991 Mathematics Subject Classification. Primary 11A25; Secondary 11-04 Key words and phrases. Factor tables, ECM, MPQS, SNFS To appear as Report NM-R96??, Centrum voor Wiskunde en Informatica, Amsterdam, March 1996. Copyright c fl 1996, the authors. Only the front matter is given here. For the tables, see rpb134u2.txt . rpb134u2 typeset using L a T E X 1 Introduction For many years there has been an interest in the prime factors of numbers of the form a n \Sigma 1, where a is a small integer (the base) and n is a positive exponent. Such numbers often arise. For example, if a is prime then there is a finite field F with a n ..