5,971 research outputs found
SqFreeEVAL: An (almost) optimal real-root isolation algorithm
Let f be a univariate polynomial with real coefficients, f in R[X].
Subdivision algorithms based on algebraic techniques (e.g., Sturm or Descartes
methods) are widely used for isolating the real roots of f in a given interval.
In this paper, we consider a simple subdivision algorithm whose primitives are
purely numerical (e.g., function evaluation). The complexity of this algorithm
is adaptive because the algorithm makes decisions based on local data. The
complexity analysis of adaptive algorithms (and this algorithm in particular)
is a new challenge for computer science. In this paper, we compute the size of
the subdivision tree for the SqFreeEVAL algorithm.
The SqFreeEVAL algorithm is an evaluation-based numerical algorithm which is
well-known in several communities. The algorithm itself is simple, but prior
attempts to compute its complexity have proven to be quite technical and have
yielded sub-optimal results. Our main result is a simple O(d(L+ln d)) bound on
the size of the subdivision tree for the SqFreeEVAL algorithm on the benchmark
problem of isolating all real roots of an integer polynomial f of degree d and
whose coefficients can be written with at most L bits.
Our proof uses two amortization-based techniques: First, we use the algebraic
amortization technique of the standard Mahler-Davenport root bounds to
interpret the integral in terms of d and L. Second, we use a continuous
amortization technique based on an integral to bound the size of the
subdivision tree. This paper is the first to use the novel analysis technique
of continuous amortization to derive state of the art complexity bounds
On the Complexity of Real Root Isolation
We introduce a new approach to isolate the real roots of a square-free
polynomial with real coefficients. It is assumed that
each coefficient of can be approximated to any specified error bound. The
presented method is exact, complete and deterministic. Due to its similarities
to the Descartes method, we also consider it practical and easy to implement.
Compared to previous approaches, our new method achieves a significantly better
bit complexity. It is further shown that the hardness of isolating the real
roots of is exclusively determined by the geometry of the roots and not by
the complexity or the size of the coefficients. For the special case where
has integer coefficients of maximal bitsize , our bound on the bit
complexity writes as which improves the best bounds
known for existing practical algorithms by a factor of . The crucial
idea underlying the new approach is to run an approximate version of the
Descartes method, where, in each subdivision step, we only consider
approximations of the intermediate results to a certain precision. We give an
upper bound on the maximal precision that is needed for isolating the roots of
. For integer polynomials, this bound is by a factor lower than that of
the precision needed when using exact arithmetic explaining the improved bound
on the bit complexity
Near-optimal perfectly matched layers for indefinite Helmholtz problems
A new construction of an absorbing boundary condition for indefinite
Helmholtz problems on unbounded domains is presented. This construction is
based on a near-best uniform rational interpolant of the inverse square root
function on the union of a negative and positive real interval, designed with
the help of a classical result by Zolotarev. Using Krein's interpretation of a
Stieltjes continued fraction, this interpolant can be converted into a
three-term finite difference discretization of a perfectly matched layer (PML)
which converges exponentially fast in the number of grid points. The
convergence rate is asymptotically optimal for both propagative and evanescent
wave modes. Several numerical experiments and illustrations are included.Comment: Accepted for publication in SIAM Review. To appear 201
The Rational Hybrid Monte Carlo Algorithm
The past few years have seen considerable progress in algorithmic development
for the generation of gauge fields including the effects of dynamical fermions.
The Rational Hybrid Monte Carlo (RHMC) algorithm, where Hybrid Monte Carlo is
performed using a rational approximation in place the usual inverse quark
matrix kernel is one of these developments. This algorithm has been found to be
extremely beneficial in many areas of lattice QCD (chiral fermions, finite
temperature, Wilson fermions etc.). We review the algorithm and some of these
benefits, and we compare against other recent algorithm developements. We
conclude with an update of the Berlin wall plot comparing costs of all popular
fermion formulations.Comment: 15 pages. Proceedings from Lattice 200
Computing Real Roots of Real Polynomials
Computing the roots of a univariate polynomial is a fundamental and
long-studied problem of computational algebra with applications in mathematics,
engineering, computer science, and the natural sciences. For isolating as well
as for approximating all complex roots, the best algorithm known is based on an
almost optimal method for approximate polynomial factorization, introduced by
Pan in 2002. Pan's factorization algorithm goes back to the splitting circle
method from Schoenhage in 1982. The main drawbacks of Pan's method are that it
is quite involved and that all roots have to be computed at the same time. For
the important special case, where only the real roots have to be computed, much
simpler methods are used in practice; however, they considerably lag behind
Pan's method with respect to complexity.
In this paper, we resolve this discrepancy by introducing a hybrid of the
Descartes method and Newton iteration, denoted ANEWDSC, which is simpler than
Pan's method, but achieves a run-time comparable to it. Our algorithm computes
isolating intervals for the real roots of any real square-free polynomial,
given by an oracle that provides arbitrary good approximations of the
polynomial's coefficients. ANEWDSC can also be used to only isolate the roots
in a given interval and to refine the isolating intervals to an arbitrary small
size; it achieves near optimal complexity for the latter task.Comment: to appear in the Journal of Symbolic Computatio
- …