In the present paper, rational wedge functions for degree two approximation have been computed over a pentagonal discretization of domain, by using an analytic approach which is extension Dasgupta’s linear approximation. This technique allows to avoid computation exterior intersection points elements, was key component initiated Wachspress. The necessary condition existence denominator function...

Neural networks can be used as function approximators to improve the energy efficiency, performance, and fault-tolerance of traditional computer architectures. To maximize these improvements the granularity of the function must be as large as possible. This work-inprogress abstract explores the lower limits of neural network function approximation by replacing individual floating point multipli...

Wavelet radiosity is, by its nature, restricted to parallelograms or triangles. This paper presents an innovative technique enabling wavelet radiosity computations on planar surfaces of arbitrary shape, including concave contours or contours with holes. This technique replaces the need for triangulating such complicated shapes, greatly reducing the complexity of the wavelet radiosity algorithm ...

This paper presents a safety-aware learning framework that employs an adaptive model learning method together with barrier certificates for systems with possibly nonstationary agent dynamics. To extract the dynamic structure of the model, we use a sparse optimization technique, and the resulting model will be used in combination with control barrier certificates which constrain feedback control...

An overview of the problem of learning to rank data is given. Some current machine learning approaches to the problem are described. The cost functions used to assess the quality of a ranking algorithm present particular difficulties: they are non-differentiable (as a function of the scores output by the ranker) and multivariate (in the sense that the cost associated with one ranked object depe...

The phase diagram of the $(1 + 1)$-dimensional Gross-Neveu model is reanalyzed for (non-)zero chemical potential and temperature within mean-field approximation. By investigating momentum dependence bosonic two-point function, well-known second-order transition from $\mathbb{Z}_2$ symmetric to so-called inhomogeneous detected. In latter chiral condensate periodically varying in space translatio...

laguerre function has many advantages such as good approximation capability for different systems, low computational complexity and the facility of on-line parameter identification. therefore, it is widely adopted for complex industrial process control. in this work, laguerre function based adaptive model predictive control algorithm (ampc) was implemented to control continuous stirred tank rea...

In this paper, we propose a new algorithm called ModelBI by blending the Bregman iterative regularization method and model function technique for solving class of nonconvex nonsmooth optimization problems. On one hand, use technique, which is essentially first-order approximation to objective function, go beyond traditional Lipschitz gradient continuity. other generate solutions fitting certain...

Set cover greedy algorithm is a natural approximation algorithm for test set problem. This paper gives a precise and tighter analysis of approximation ratio of this algorithm. The author improves the approximation ratio 2 lnn directly derived from set cover to 1.14 lnn by applying potential function technique of derandomization method. In addition, the author gives a nontrivial lower bound (1+α...