On the Complexity of Solving Zero-Dimensional Polynomial Systems via Projection

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. The theoretical analysis of our approach shows that the overall cost for the two problems considered above is dominated by the cost of carrying out the projections. We also give bounds on the bit complexity of our algorithms that are exclusively stated in terms of the number of variables, the total degree and the bitsize of the input polynomials

Computing Real Roots of Real Polynomials -- An Efficient Method Based on Descartes' Rule of Signs and Newton Iteration

Computing the real roots of a polynomial is a fundamental problem of computational algebra. We describe a variant of the Descartes method that isolates the real roots of any real square-free polynomial given through coefficient oracles. A coefficient oracle provides arbitrarily good approximations of the coefficients. The bit complexity of the algorithm matches the complexity of the best algorithm known, and the algorithm is simpler than this algorithm. The algorithm derives its speed from the combination of Descartes method with Newton iteration. Our algorithm can also be used to further refine the isolating intervals to an arbitrary small size. The complexity of root refinement is nearly optimal

On the complexity of the {Descartes} method when using approximate arithmetic

In this paper, we introduce a variant of the Descartes method to isolate the real roots of a square-free polynomial F ( x ) = ∑ i = 0 n A i x i with arbitrary real coefficients. It is assumed that each coefficient of F can be approximated to any specified error bound. Our algorithm uses approximate arithmetic only, nevertheless, it is certified, complete and deterministic. We further provide a bound on the complexity of our method which exclusively depends on the geometry of the roots and not on the complexity of the coefficients of F. For the special case, where F is a polynomial of degree n with integer coefficients of maximal bitsize τ, our bound on the bit complexity writes as O ˜ ( n 3 τ 2 ) . Compared to the complexity of the classical Descartes method from Collins and Akritas (based on ideas dating back to Vincent), which uses exact rational arithmetic, this constitutes an improvement by a factor of n. The improvement mainly stems from the fact that the maximal precision that is needed for isolating the roots of F is by a factor n lower than the precision needed when using exact arithmetic.Abstract Keywords 1. Introduction 2. Preliminaries 3. A modified Descartes method 4. Algorithm 5. Conclusion Acknowledgements Appendix A. Reference

A Worst-Case Bound for Topology Computation of Algebraic Curves

AbstractComputing the topology of an algebraic plane curve C means computing a combinatorial graph that is isotopic to C and thus represents its topology in R2. We prove that, for a polynomial of degree n with integer coefficients bounded by 2ρ, the topology of the induced curve can be computed with Õ(n8ρ(n+ρ)) bit operations (Õ indicates that we omit logarithmic factors). Our analysis improves the previous best known complexity bounds by a factor of n2. The improvement is based on new techniques to compute and refine isolating intervals for the real roots of polynomials, and on the consequent amortized analysis of the critical fibers of the algebraic curve

Fast Approximate Polynomial Multipoint Evaluation and Applications

It is well known that, using fast algorithms for polynomial multiplication and division, evaluation of a polynomial F ∈ C[x] of degree n at n complex-valued points can be done with Õ(n) exact field operations in C, where Õ(·) means that we omit polylogarithmic factors. We complement this result by an analysis of approximate multipoint evaluation of F to a precision of L bits after the binary point and prove a bit complexity of Õ(n(L + τ + nΓ)), where 2τ and 2Γ, with τ, Γ ∈ N≥1, are bounds on the magnitude of the coefficients of F and the evaluation points, respectively. In particular, in the important case where the precision demand dominates the other input parameters, the complexity is soft-linear in n and L. Our result on approximate multipoint evaluation has some interesting consequences on the bit complexity of three further approximation algorithms which all use polynomial evaluation as a key subroutine. This comprises an algorithm to approximate the real roots of a polynomial, an algorithm for polynomial interpolation, and a method for computing a Taylor shift of a polynomial. For all of the latter algorithms, we derive near optimal running times

A Generic and Flexible Framework for the Geometrical and Topological Analysis of (Algebraic) Surfaces

We present a generic framework on a set of surfaces S in R^3 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, that is, 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 A"S with certain properties. Second, for each of its components, a sample point is lifted back to R^3 while detecting intersections with the given surfaces. For the projection we rely on Cgal's Arrangement_2 package as basic tool. Anyhow, 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. We show that the well-known family of (semi-)algebraic surfaces fulfills the framework's requirements. As robust implementations on these surfaces are lacking these days, we consider the framework to be an important step to fill this gap. In particular, we instantiate the framework by a fully-fledged model for special algebraic surfaces, namely quadrics. This instantiation already supports main tasks demanded from rotational robot motion planning, for example, as expected to compute a Piano Mover's instance

Computing Real Roots of Real Polynomials \ldots{} and now For Real!

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. With an excerpt of our extensive collection of benchmarks, available online at http://anewdsc.mpi-inf.mpg.de/, we illustrate that the theoretical gain in performance of ANewDsc over other subdivision methods also transfers into practice. These experiments also show that our new implementation outperforms both RS and mature competitors by magnitudes for notoriously hard instances with clustered roots. For all other instances, we avoid almost any overhead by integrating additional optimizations and heuristics