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Item type:Publication, Access status: without files , Complexity of a root clustering algorithm for holomorphic functionsApproximating the roots of a holomorphic function in an input box is a fundamental problem in many domains. Most algorithms in the literature for solving this problem are conditional, i.e., they make some simplifying assumptions, such as, all the roots are simple or there are no roots on the boundary of the input box, or the underlying machine model is Real RAM. Root clustering is a generalization of the root approximation problem that allows for errors in the computation and makes no assumption on the multiplicity of the roots. An unconditional algorithm for computing a root clustering of a holomorphic function was given by Yap, Sagraloff and Sharma in 2013 [36]. They proposed a subdivision based algorithm using effective predicates based on Pellet's test while avoiding any comparison with zeros (using soft zero comparisons instead). In this paper, we analyze the running time of their algorithm. We use the continuous amortization framework to derive an upper bound on the size of the subdivision tree. We specialize this bound to the case of polynomials and some simple transcendental functions such as exponential and trigonometric sine. We show that the algorithm takes exponential time even for these simple functions, unlike the case of polynomials. We also derive a bound on the bit-precision used by the algorithm. To the best of our knowledge, this is the first such result for holomorphic functions. We introduce new geometric parameters, such as the relative growth of the function on the input box, for analyzing the algorithm. Thus, our estimates naturally generalize the known results, i.e., for the case of polynomials.Publicationtype: Journal Article23 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Near optimal subdivision algorithms for real root isolation(2017-11); Isolating real roots of a square-free polynomial in a given interval is a fundamental problem in computational algebra. Subdivision based algorithms are a standard approach to solve this problem. For instance, Sturm's method, or various algorithms based on the Descartes's rule of signs. For the benchmark problem of isolating all the real roots of a polynomial of degree n and root separation σ, the size of the subdivision tree of most of these algorithms is bounded by O(log1/σ) (assume σ<1). Moreover, it is known that this is optimal for subdivision algorithms that perform uniform subdivision, i.e., the width of the interval decreases by some constant. Recently Sagraloff (2012) and Sagraloff–Mehlhorn (2016) have developed algorithms for real root isolation that combine subdivision with Newton iteration to reduce the size of the subdivision tree to O(n(log(nlog1/σ))). We describe a subroutine that reduces the size of the subdivision tree of any subdivision algorithm for real root isolation. The subdivision tree size of our algorithm using predicates based on either the Descartes's rule of signs or Sturm sequences is bounded by O(nlogn), which is close to the optimal value of O(n). The corresponding bound for the algorithm EVAL, which uses certain interval-arithmetic based predicates, is O(n2logn). Our analysis differs in two key aspects from earlier approaches. First, we use the general technique of continuous amortization from Burr–Krahmer–Yap (2009), and extend it to handle non-uniform subdivisions; second, we use the geometry of clusters of roots instead of root bounds. The latter aspect enables us to derive a bound on the subdivision tree that is independent of the root separation σ. The number of Newton iterations is bounded by O(nloglog(1/σ)).Publicationtype: Journal Article111 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Near optimal subdivision algorithms for real root isolationIsolating real roots of a square-free polynomial in a given interval is a fundamental problem. Subdivision based algorithms are a standard approach to solve this problem. E.g., Sturm's method, or various algorithms based on the Descartes's rule of signs. For isolating all the real roots of a degree n polynomial with root separation σ, the subdivision tree size of most of these algorithms is bounded by O(log 1/σ) (assume σ < 1). Recently Sagraloff (2012) and Sagraloff-Mehlhorn (2013) have developed algorithms that combine subdivision with Newton iteration to reduce the size of the subdivision tree to O(n(log(n log 1/σ))). Their algorithms and analysis crucially depend on the terminating predicates. We describe a subroutine that improves the running time of any subdivision algorithm for real root isolation. The subdivision tree size of our algorithm using predicates based on the Descartes's rule of signs is bounded by O(n log n). Our analysis differs in two key aspects from earlier approaches. First, we use the general technique of continuous amortization from Burr-Krahmer-Yap (2009), and hence the analysis extends to other predicates; and second, we use the geometry of clusters of roots instead of root bounds.Publicationtype: Conference Paper28