In this paper a numerical approach for the optimization of stirrer configurations is presented. The methodology is based on a flow solver, and a mathematical optimization tool, which are integrated into an automated procedure. The flow solver is based on the discretization of the incompressible Navier-Stokes equations by means of a fully conservative finite-volume method for block-structured, b...

This paper describe a system-level approach to improve the area and delay of datapath designs that perform polynomial computations over , which are used in many applications such as computer graphics and digital signal processing domains. This approach optimizes the implementation of multivariate polynomial systems in terms of the number of arithmetic operations by performing optimization on a ...

We consider stochastic programs where the distribution of the uncertain parameters is only observable through a finite training dataset. Using the Wasserstein metric, we construct a ball in the space of (multivariate and non-discrete) probability distributions centered at the uniform distribution on the training samples, and we seek decisions that perform best in view of the worst-case distribu...

We propose a multidimentional time-point model and algorithm to solve Multi-Event Expert Query Parametric Estimation (ME-EQPE) problems over multivariate time series. Our proposed model and algorithm combine the strengths of both domain-knowledge-based and formal-learning-based approaches to learn optimal decision parameters for maximizing utility over multivariate time series. More specificall...

Principal Component Analysis is a technique often found to be useful for identifying structure in multivariate data. Although it has various characterizations (Rao 1964), the most familiar is as a variance-maximizing projection. Projection pursuit is a methodology for selecting low-dimensional projections of multivariate data by the optimization of some index of \interestingness" over all proje...

We describe the design, implementation and experimental evaluation of new algorithms for computing the approximate factorization of multivariate polynomials with complex coefficients that contain numerical noise. Our algorithms are based on a generalization of the differential forms introduced by W. Ruppert and S. Gao to many variables, and use singular value decomposition or structured total l...

We propose a new method for a nonparametric estimation of Rényi and Shannon information for a multivariate distribution using a corresponding copula, a multivariate distribution over normalized ranks of the data. As the information of the distribution is the same as the negative entropy of its copula, our method estimates this information by solving a Euclidean graph optimization problem on the...