In this paper, we propose a symbolic-numeric algorithm to count the number of solutions of a zero-dimensional square polynomial system within a local region. We show that the algorithm succeeds under the condition that the region is sufficiently small and well-isolating for a k-fold solution z of the system. In our analysis, we derive a bound on the size of the region that guarantees success. We further argue that this size depends on local parameters such as the norm and multiplicity of z as well as the distances between z and all other solutions. Efficiency of our method stems from the fact that we reduce the problem of counting the roots of the original system to the problem of solving a truncated system of degree k. In particular, if the multiplicity k of z is small compared to the total degrees of the original polynomials, our method considerably improves upon known complete and certified methods. We see a series of applications of our approach. When combined with a numerical solver in the fashion of an a posteriori certification step, we obtain a certified and reliable method for solving polynomial systems while profiting both from the efficiency of the numerical algorithm and the reliability of the symbolic approach. An alternative application results from incorporating our algorithm as inclusion predicate into an elimination method. For the special case of bivariate systems, we experimentally show that this approach leads to a significant improvement over an existing state-of-the-art elimination method.

We propose an efficient algorithm to compute the real roots of a sparse polynomial f∈R[x] having k non-zero real-valued coefficients. It is assumed that arbitrarily good approximations of the non-zero coefficients are given by means of a coefficient oracle. For a given positive integer L, our algorithm returns disjoint disks Δ1,...,Δs⊂C, with s<2k, centered at the real axis and of radius less than 2-L together with positive integers μ1,...,μs such that each disk Δi contains exactly μi roots of f counted with multiplicity. In addition, it is ensured that each real root of f is contained in one of the disks. If f has only simple real roots, our algorithm can also be used to isolate all real roots.

Let F(z) be an arbitrary complex polynomial. We introduce the {local root clustering problem}, to compute a set of natural epsilon-clusters of roots of F(z) in some box region B0 in the complex plane. This may be viewed as an extension of the classical root isolation problem. Our contribution is two-fold: we provide an efficient certified subdivision algorithm for this problem, and we provide a bit-complexity analysis based on the local geometry of the root clusters.

Given a zero-dimensional polynomial system consisting of n integer polynomials in n variables, we propose a certified and complete method to compute all complex solutions of the system as well as a corresponding separating linear form l with coefficients of small bit size. For computing l, we need to project the solutions into one dimension along O(n) distinct directions but no further algebraic manipulations. The solutions are then directly reconstructed from the considered projections. The first step is deterministic, whereas the second step uses randomization, thus being Las Vegas.

Very recent work introduces an asymptotically fast subdivision algorithm, denoted ANewDsc, for isolating the real roots of a univariate real polynomial. The method combines Descartes? Rule of Signs to test intervals for the existence of roots, Newton iteration to speed up convergence against clusters of roots, and approximate computation to decrease the required precision. It achieves record bounds on the worst-case complexity for the considered problem, matching the complexity of Pan's method for computing all complex roots and improving upon the complexity of other subdivision methods by several magnitudes. In the article at hand, we report on an implementation of ANewDsc on top of the RS root isolator. RS is a highly efficient realization of the classical Descartes method and currently serves as the default real root solver in Maple. We describe crucial design changes within ANewDsc and RS that led to a high-performance implementation without harming the theoretical complexity of the underlying algorithm.

Let p ∈ Z[x] be an arbitrary polynomial of degree n with k non-zero integer coefficients of absolute value less than 2τ. In this paper, we answer the open question whether the real roots of p can be computed with a number of arithmetic operations over the rational numbers that is polynomial in the input size of the sparse representation of p. More precisely, we give a deterministic, complete, and certified algorithm that determines isolating intervals for all real roots of p with O(k3·log(nτ)·logn) many exact arithmetic operations over the rational numbers.

We present an algorithm for isolating all roots of an arbitrary complex polynomial p which also works in the presence of multiple roots provided that arbitrary good approximations of the coefficients of p and the number of distinct roots are given. Its output consists of pairwise disjoint disks each containing one of the distinct roots of p, and its multiplicity. The algorithm uses approximate factorization as a subroutine. For the case, where Pan's algorithm [16] is used for the factorization, we derive complexity bounds for the problems of isolating and refining all roots which are stated in terms of the geometric locations of the roots only. Specializing the latter bounds to a polynomial of degree d and with integer coefficients of bitsize less than τ, we show that Õ(d3+d2τ+dκ) bit operations are sufficient to compute isolating disks of size less than 2-κ for all roots of p, where κ is an arbitrary positive integer.

We study the complexity of computing the real solutions of a bivariate polynomial system using the recently presented algorithm Bisolve [2]. Bisolve is an elimination method which, in a first step, projects the solutions of a system onto the x- and y-axes and, then, selects the actual solutions from the so induced candidate set. However, unlike similar algorithms, Bisolve requires no genericity assumption on the input, and there is no need for any kind of coordinate transformation. Furthermore, extensive benchmarks as presented in [2] confirm that the algorithm is highly practical, that is, a corresponding C++ implementation in Cgal outperforms state of the art approaches by a large factor. In this paper, we focus on the theoretical complexity of Bisolve. For two polynomials f, g ∈ Z[x, y] of total degree at most n with integer coefficients bounded by 2τ, we show that Bisolve computes isolating boxes for all real solutions of the system f = g = 0 using O(n8 + n7τ) bit operations, thereby improving the previous record bound for the same task by several magnitudes.

We introduce a novel algorithm denoted NewDsc to isolate the real roots of a univariate square-free polynomial f with integer coefficients. The algorithm iteratively subdivides an initial interval which is known to contain all real roots of f and performs exact (rational) operations on the coefficients of f in each step. For the subdivision strategy, we combine Descartes' Rule of Signs and Newton iteration. More precisely, instead of using a fixed subdivision strategy such as bisection in each iteration, a Newton step based on the number of sign variations for an actual interval is considered, and, only if the Newton step fails, we fall back to bisection. Following this approach, quadratic convergence towards the real roots is achieved in most iterations. In terms of complexity, our method induces a recursion tree of almost optimal size O(n·log(nτ)), where n denotes the degree of the polynomial and τ the bitsize of its coefficients. The latter bound constitutes an improvement by a factor of τ upon all existing subdivision methods for the task of isolating the real roots. We further provide a detailed complexity analysis which shows that NewDsc needs only Õ(n3τ) bit operations to isolate all real roots of f. In comparison to existing asymptotically fast numerical algorithms (e.g. the algorithms by V. Pan and A. Schönhage), NewDsc is much easier to access and, due to its similarities to the classical Descartes method, it seems to be well suited for an efficient implementation.

We present an exact and complete algorithm to isolate the real solutions of a zero-dimensional bivariate polynomial system. The proposed algorithm constitutes an elimination method which improves upon existing approaches in a number of points. First, the amount of purely symbolic operations is significantly reduced, that is, only resultant computation and square-free factorization is still needed. Second, our algorithm neither assumes generic position of the input system nor demands for any change of the coordinate system. The latter is due to a novel inclusion predicate to certify that a certain region is isolating for a solution. Our implementation exploits graphics hardware to expedite the resultant computation. Furthermore, we integrate a number of filtering techniques to improve the overall performance. Efficiency of the proposed method is proven by a comparison of our implementation with two state-of-the-art implementations, that is, Lgp and Maple's Isolate. For a series of challenging benchmark instances, experiments show that our implementation outperforms both contestants.

We consider the problem of approximating all real roots of a square-free polynomial f. Given isolating intervals, our algorithm refines each of them to a width at most 2-L, that is, each of the roots is approximated to L bits after the binary point. Our method provides a certified answer for arbitrary real polynomials, only requiring finite approximations of the polynomial coefficient and choosing a suitable working precision adaptively. In this way, we get a correct algorithm that is simple to implement and practically efficient. Our algorithm uses the quadratic interval refinement method; we adapt that method to be able to cope with inaccuracies when evaluating f, without sacrificing its quadratic convergence behavior. We prove a bound on the bit complexity of our algorithm in terms of degree, coefficient size and discriminant. Our bound improves previous work on integer polynomials by a factor of deg f and essentially matches best known theoretical bounds on root approximation which are obtained by very sophisticated algorithms.

We present a new exact subdivision algorithm CEVAL for isolating the complex roots of a square-free polynomial in any given box. It is a generalization of a previous real root isolation algorithm called EVAL. Under suitable conditions, our approach is applicable for general analytic functions. CEVAL is based on the simple Bolzano Principle and is easy to implement exactly. Preliminary experiments have shown its competitiveness.

We describe a bisection algorithm for root isolation of polynomials with real coefficients. It is assumed that the coefficients can be approximated with arbitrary precision; exact computation in the field of coefficients is not required. We refer to such coefficients as bitstream coefficients. The algorithm is deterministic and has almost the same asymptotic complexity as the randomized algorithm of [12]. We also discuss a partial extension to multiple roots.

We present a method to compute the exact topology of a real algebraic surface S, implicitly given by a polynomial f ∈ Q[x;y;z] of arbitrary degree N. Additionally, our analysis provides geometric information as it supports the computation of arbitrary precise samples of S including critical points. We use a projection approach, similar to Collins' cylindrical algebraic decomposition (cad). In comparison we reduce the number of output cells to O(N5) by constructing a special planar arrangement instead of a full cad in the projection plane. Furthermore, our approach applies numerical and combinatorial methods to minimize costly symbolic computations. The algorithm handles all sorts of degeneracies without transforming the surface into a generic position. We provide a complete implementation of the algorithm, written in C++. It shows good performance for many well known examples from algebraic geometry.

We present a generic framework on a set of surfaces S in R3 that provides their geometric and topological analysis in order to support various algorithms and applications in computational geometry. Our implementation follows the generic programming paradigm, i.e., to support a certain family of surfaces, we require a small set of types and some basic operations on them, all collected in a model of the newly presented SURFACETRAITS_3 concept. The framework obtains geometric and topological information on a non-empty set of surfaces in two steps. First, important 0-and 1-dimensional features are projected onto the xy-plane, obtaining an arrangement As with certain properties. Second, for each of its components, a sample point is lifted back to R3 while detecting intersections with the given surfaces. This idea is similar to Collins' cylindrical algebraic decomposition (cad). In contrast, we reduce the number of liftings using CGAL'S Arrangement_2 package as a basic tool. Properly instantiated, the framework provides main functionality required to support the computation of a Piano Mover's instance. On the other hand, the complexity of the output is high, and thus, we particularly regard the framework as key ingredient for querying information on and constructing geometric objects from a small set of surfaces. Examples are meshing of single surfaces, the computation of space-curves defined by two surfaces, to compute lower envelopes of surfaces, or as a basic step to compute an efficient representation of a three-dimensional arrangement.