7 research outputs found

    The construction of good lattice rules and polynomial lattice rules

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    A comprehensive overview of lattice rules and polynomial lattice rules is given for function spaces based on ℓp\ell_p semi-norms. Good lattice rules and polynomial lattice rules are defined as those obtaining worst-case errors bounded by the optimal rate of convergence for the function space. The focus is on algebraic rates of convergence O(N−α+ϵ)O(N^{-\alpha+\epsilon}) for α≥1\alpha \ge 1 and any ϵ>0\epsilon > 0, where α\alpha is the decay of a series representation of the integrand function. The dependence of the implied constant on the dimension can be controlled by weights which determine the influence of the different dimensions. Different types of weights are discussed. The construction of good lattice rules, and polynomial lattice rules, can be done using the same method for all 1<p≤∞1 < p \le \infty; but the case p=1p=1 is special from the construction point of view. For 1<p≤∞1 < p \le \infty the component-by-component construction and its fast algorithm for different weighted function spaces is then discussed

    MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

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    Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

    Generalized averaged Gaussian quadrature and applications

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    A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

    Uncertainty in Artificial Intelligence: Proceedings of the Thirty-Fourth Conference

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