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 ...

Nikolay Bliznyuk, David Ruppert, Christine Shoemaker, Rommel Regis, Stefan Wild, Pradeep Mugunthan, Bayesian Calibration and Uncertainty Analysis for Computationally Expensive Models Using ...

Matrix Functions and Their Approximations Publication Trend The graph below shows the total number of publications each year in Matrix Functions and Their Approximations.

We deal with a method of enhanced convergence for the approximation of analytic functions. This method introduces conformal transformations in the approximation problems, in order to help extract the ...

This is a hands-on graduate-level course teaching key techniques to analyse and estimate macroeconomics models. It teaches the key building blocks of numerical analysis such as function approximation ...

Value function approximation or stopping time approximation: a comparison of two recent numerical methods for American option pricing using simulation and regression Lars Stentoft ...

Monte Carlo simulation techniques that use function approximations have been successfully applied to approximately price multi-dimensional American options. However, for many pricing problems the time ...

Approximation in such a low-rank format renders these functions easily integrable as it breaks the integration problem to the sum of products of one- or two-dimensional integrals, so standard ...