Gradual sub-lattice reduction and a new complexity for factoring polynomials

Computer Science – Symbolic Computation

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

We present a lattice algorithm specifically designed for some classical applications of lattice reduction. The applications are for lattice bases with a generalized knapsack-type structure, where the target vectors are boundably short. For such applications, the complexity of the algorithm improves traditional lattice reduction by replacing some dependence on the bit-length of the input vectors by some dependence on the bound for the output vectors. If the bit-length of the target vectors is unrelated to the bit-length of the input, then our algorithm is only linear in the bit-length of the input entries, which is an improvement over the quadratic complexity floating-point LLL algorithms. To illustrate the usefulness of this algorithm we show that a direct application to factoring univariate polynomials over the integers leads to the first complexity bound improvement since 1984. A second application is algebraic number reconstruction, where a new complexity bound is obtained as well.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Gradual sub-lattice reduction and a new complexity for factoring polynomials does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Gradual sub-lattice reduction and a new complexity for factoring polynomials, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Gradual sub-lattice reduction and a new complexity for factoring polynomials will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-602138

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.