Function approximation, a central theme in numerical analysis and applied mathematics, seeks to represent complex functions through simpler or more computationally tractable forms. In this context, ...

This is a preview. Log in through your library . Abstract Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The ...

Neural network approximation techniques have emerged as a formidable approach in computational mathematics and machine learning, providing robust tools for approximating complex functions. By ...

Brilliant figures such as Sergey Nikitovich Mergelyan (1928-2008) defined the 20th century as an era for extraordinary ...

The FD= and FDHESSIAN= options specify the use of finite difference approximations of the derivatives. The FD= option specifies that all derivatives are approximated using function evaluations, and ...

This paper presents a saddlepoint approximation to the cumulative distribution function of a random vector. The proposed approximation has accuracy comparable to that of existing expansions valid in ...

The area \(A\) of a square of side length \(s\) is \(A=s^2\text{.}\) Suppose \(s\) increases by an amount \(\Delta s=ds\text{.}\) Draw a square and then illustrate ...