We present a new barrier function, based on a kernel function with a linear growth term and an inverse linear barrier term. Existing kernel functions have a quadratic (or higher degree) growth term, and a barrier term that is either transcendent (e.g. logarithmic) or of a more complicated algebraic form. So the new kernel function has the simplest possible form compared with all existing kernel...

Kernel methods are an extremely popular set of techniques used for many important machine learning and data analysis applications. In addition to having good practical performance, these methods are supported by a well-developed theory. Kernel methods use an implicit mapping of the input data into a high dimensional feature space defined by a kernel function, i.e., a function returning the inne...

We discuss the symmetric smoothed particle hydrodynamics (SSPH) method for generating basis functions for a meshless method. It admits a larger class of kernel functions than some other methods, including the smoothed particle hydrodynamics (SPH), the modified smoothed particle hydrodynamics (MSPH), the reproducing kernel particle method (RKPM), and the moving least squares (MLS) methods. For f...

This paper is concerned with a study of special integral equation. equation arises in many applied problems, including transmutation theory, inverse scattering the solution singular Sturm–Liouville and Shrödinger equations, representation solutions equations. A derived formulated using Riemann function hyperbolic In paper, existence unique to this proven by method successive approximations. The...