This work presents a new hybrid symbolic-numeric method for fast and accurate evaluation of multiple integrals, effective both in high dimensions and with high accuracy. In two dimensions, the thesis presents an adaptive two-phase algorithm for double integration of continuous functions over general regions using Frederick W. Chapman's recently developed Geddes series expansions to approximate the integrand. These results are extended to higher dimensions using a novel Deconstruction/Approximation/Reconstruction Technique (DART), which facilitates the dimensional reduction of families of integrands with special structure over hyperrectangular regions. The thesis describes a Maple implementation of these new methods and presents empirical results and conclusions from extensive testing. Various alternatives for implementation are discussed, and the new methods are compared with existing numerical and symbolic methods for multiple integration. The thesis concludes that for some frequently encountered families of integrands, DART breaks the curse of dimensionality that afflicts numerical integration.

This work presents a new hybrid symbolic-numeric method for fast and accurate evaluation of multiple integrals, effective both in high dimensions and with high accuracy. In two dimensions, the thesis presents an adaptive two-phase algorithm for double integration of continuous functions over general regions using Frederick W. Chapman's recently developed Geddes series expansions to approximate the integrand. These results are extended to higher dimensions using a novel Deconstruction/Approximation/Reconstruction Technique (DART), which facilitates the dimensional reduction of families of integrands with special structure over hyperrectangular regions. The thesis describes a Maple implementation of these new methods and presents empirical results and conclusions from extensive testing. Various alternatives for implementation are discussed, and the new methods are compared with existing numerical and symbolic methods for multiple integration. The thesis concludes that for some frequently encountered families of integrands, DART breaks the curse of dimensionality that afflicts numerical integration.

High-dimensional integrals arise in a variety of areas, including quantum physics, the physics and chemistry of molecules, statistical mechanics and more recently, in financial applications. In order to approximate multidimensional integrals, one may use Monte Carlo methods in which the quadrature points are generated randomly or quasi-Monte Carlo methods, in which points are generated deterministically. One particular class of quasi-Monte Carlo methods for multivariate integration is represented by lattice rules. Lattice rules constructed throughout this thesis allow good approximations to integrals of functions belonging to certain weighted function spaces. These function spaces were proposed as an explanation as to why integrals in many variables appear to be successfully approximated although the standard theory indicates that the number of quadrature points required for reasonable accuracy would be astronomical because of the large number of variables. The purpose of this thesis is to contribute to theoretical results regarding the construction of lattice rules for multiple integration. We consider both lattice rules for integrals over the unit cube and lattice rules suitable for integrals over Euclidean space. The research reported throughout the thesis is devoted to finding the generating vector required to produce lattice rules that have what is termed a low weighted discrepancy . In simple terms, the discrepancy is a measure of the uniformity of the distribution of the quadrature points or in other settings, a worst-case error. One of the assumptions used in these weighted function spaces is that variables are arranged in the decreasing order of their importance and the assignment of weights in this situation results in so-called product weights . In other applications it is rather the importance of group of variables that matters. This situation is modelled by using function spaces in which the weights are general . In the weighted settings mentioned above, the quality of the lattice rules is assessed by the weighted discrepancy mentioned earlier. Under appropriate conditions on the weights, the lattice rules constructed here produce a convergence rate of the error that ranges from O(n−1/2) to the (believed) optimal O(n−1+δ) for any δ gt 0, with the involved constant independent of the dimension.