We introduce an exact subdivision algorithm CEVAL for isolating complex roots of a square-free polynomial. The subdivision predicates are based on evaluating the original polynomial or its derivatives, and hence is easy to implement. It can be seen as a generalization of a previous real root isolation algorithm called EVAL. Under suitable conditions, the algorithm is applicable for general analytic functions. We provide a complexity analysis of our algorithm on the benchmark problem of isolating all complex roots of a square-free polynomial with Gaussian integer coefficients. The analysis is based on a novel technique called δ-clusters. This analysis shows, somewhat surprisingly, that the simple EVAL algorithm matches (up to logarithmic factors) the bit complexity bounds of current practical exact algorithms such as those based on Descartes, Continued Fraction or Sturm methods. Furthermore, the more general CEVAL also achieves the same complexity.

The dynamic convex hull problem was recently solved in O(lg n) time per operation, and this result is best possible in models of computation with bounded branching (e.g., algebraic computation trees). From a data structures point of view, however, such models are considered unrealistic because they hide intrinsic notions of information in the input. In the standard word-RAM and cell-probe models of computation, we prove that the optimal query time for dynamic convex hulls is, in fact, Θ ` ´ lg n, for polylogarithmic up-lg lg n date time (and word size). Our lower bound is based on a reduction from the marked-ancestor problem, and is one of the first data structural lower bounds for a nonorthogonal geometric problem. Our upper bounds follow a recent trend of attacking nonorthogonal geometric problems from an information-theoretic perspective that has proved central to advanced data structures. Interestingly, our upper bounds are the first to successfully apply this perspective to dynamic geometric data structures, and require substantially different ideas from previous work.

The dynamic convex hull problem was recently solved in O(lg n) time per operation, and this result is best possible in models of computation with bounded branching (e.g., algebraic computation trees). From a data structures point of view, however, such models are considered unrealistic because they hide intrinsic notions of information in the input. In the standard word-RAM and cell-probe models of computation, we prove that the optimal query time for dynamic convex hulls is, in fact, Θ ` ´ lg n, for polylogarithmic up-lg lg n date time (and word size). Our lower bound is based on a reduction from the marked-ancestor problem, and is one of the first data structural lower bounds for a nonorthogonal geometric problem. Our upper bounds follow a recent trend of attacking nonorthogonal geometric problems from an information-theoretic perspective that has proved central to advanced data structures. Interestingly, our upper bounds are the first to successfully apply this perspective to dynamic geometric data structures, and require substantially different ideas from previous work.