385 research outputs found
Quasi-GCD computations
AbstractFor univariate polynomials with real or complex coefficients and a given error bound Ï” > 0, h is called a quasi-gcd of f and g, if h is an Ï”-approximate divisor of f and of g and if any (exact) common divisor of f, g is an approximate divisor of h. Extended quasi-gcd computation means to find such h and additional cofactors u, Îœ such that | uf + Îœg â h | < Ï” | h | holds. Suitable âpivotingâ leads to a numerically stable version of Euclid's algorithm for solving this task. Further refinements by a divide-and-conquer technique and by means of fast algorithms for polynomial arithmetic then yield the worst case upper bound O(n2 lg n(lg(1/Ï”) + n lg n)) of âpointer timeâ for nth-degree polynomials. In the particular case of integer polynomials, however, an immediate reduction to fast integer gcd computation is recommended, instead
An O(M(n) log n) algorithm for the Jacobi symbol
The best known algorithm to compute the Jacobi symbol of two n-bit integers
runs in time O(M(n) log n), using Sch\"onhage's fast continued fraction
algorithm combined with an identity due to Gauss. We give a different O(M(n)
log n) algorithm based on the binary recursive gcd algorithm of Stehl\'e and
Zimmermann. Our implementation - which to our knowledge is the first to run in
time O(M(n) log n) - is faster than GMP's quadratic implementation for inputs
larger than about 10000 decimal digits.Comment: Submitted to ANTS IX (Nancy, July 2010
Finding the median
An algorithm is described which determines the median of n elements using in the worst case a number of comparison asymptotic to 3n
How Fast Can We Multiply Large Integers on an Actual Computer?
We provide two complexity measures that can be used to measure the running
time of algorithms to compute multiplications of long integers. The random
access machine with unit or logarithmic cost is not adequate for measuring the
complexity of a task like multiplication of long integers. The Turing machine
is more useful here, but fails to take into account the multiplication
instruction for short integers, which is available on physical computing
devices. An interesting outcome is that the proposed refined complexity
measures do not rank the well known multiplication algorithms the same way as
the Turing machine model.Comment: To appear in the proceedings of Latin 2014. Springer LNCS 839
Elliptic periods for finite fields
We construct two new families of basis for finite field extensions. Basis in
the first family, the so-called elliptic basis, are not quite normal basis, but
they allow very fast Frobenius exponentiation while preserving sparse
multiplication formulas. Basis in the second family, the so-called normal
elliptic basis are normal basis and allow fast (quasi linear) arithmetic. We
prove that all extensions admit models of this kind
Accelerating the CM method
Given a prime q and a negative discriminant D, the CM method constructs an
elliptic curve E/\Fq by obtaining a root of the Hilbert class polynomial H_D(X)
modulo q. We consider an approach based on a decomposition of the ring class
field defined by H_D, which we adapt to a CRT setting. This yields two
algorithms, each of which obtains a root of H_D mod q without necessarily
computing any of its coefficients. Heuristically, our approach uses
asymptotically less time and space than the standard CM method for almost all
D. Under the GRH, and reasonable assumptions about the size of log q relative
to |D|, we achieve a space complexity of O((m+n)log q) bits, where mn=h(D),
which may be as small as O(|D|^(1/4)log q). The practical efficiency of the
algorithms is demonstrated using |D| > 10^16 and q ~ 2^256, and also |D| >
10^15 and q ~ 2^33220. These examples are both an order of magnitude larger
than the best previous results obtained with the CM method.Comment: 36 pages, minor edits, to appear in the LMS Journal of Computation
and Mathematic
Combining All Pairs Shortest Paths and All Pairs Bottleneck Paths Problems
We introduce a new problem that combines the well known All Pairs Shortest
Paths (APSP) problem and the All Pairs Bottleneck Paths (APBP) problem to
compute the shortest paths for all pairs of vertices for all possible flow
amounts. We call this new problem the All Pairs Shortest Paths for All Flows
(APSP-AF) problem. We firstly solve the APSP-AF problem on directed graphs with
unit edge costs and real edge capacities in
time,
where is the number of vertices, is the number of distinct edge
capacities (flow amounts) and is the time taken
to multiply two -by- matrices over a ring. Secondly we extend the problem
to graphs with positive integer edge costs and present an algorithm with
worst case time complexity, where is
the upper bound on edge costs
Rapid computation of L-functions for modular forms
Let be a fixed (holomorphic or Maass) modular cusp form, with
-function . We describe an algorithm that computes the value
to any specified precision in time
A Randomized Sublinear Time Parallel GCD Algorithm for the EREW PRAM
We present a randomized parallel algorithm that computes the greatest common
divisor of two integers of n bits in length with probability 1-o(1) that takes
O(n loglog n / log n) expected time using n^{6+\epsilon} processors on the EREW
PRAM parallel model of computation. We believe this to be the first randomized
sublinear time algorithm on the EREW PRAM for this problem
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