About optimization of methods for mixed derivatives of bivariate functions

09/18/2023
by   Y. V. Semenova, et al.
0

The problem of optimal recovering high-order mixed derivatives of bivariate functions with finite smoothness is studied. On the basis of the truncation method, an algorithm for numerical differentiation is constructed, which is order-optimal both in the sense of accuracy and in terms of the amount of involved Galerkin information.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/11/2023

On optimal recovering high order partial derivatives of bivariate functions

The problem of recovering partial derivatives of high orders of bivariat...
research
08/15/2023

On regularized Radon-Nikodym differentiation

We discuss the problem of estimating Radon-Nikodym derivatives. This pro...
research
01/01/2022

On automatic differentiation for the Matérn covariance

To target challenges in differentiable optimization we analyze and propo...
research
02/02/2020

The Discrete Adjoint Method: Efficient Derivatives for Functions of Discrete Sequences

Gradient-based techniques are becoming increasingly critical in quantita...
research
05/11/2020

Towards 1ULP evaluation of Daubechies Wavelets

We present algorithms to numerically evaluate Daubechies wavelets and sc...
research
02/11/2019

Efficient Computation of High-Order Electromagnetic Field Derivatives for Multiple Design Parameters in FDTD

This paper introduces a new computational framework to derive electromag...
research
05/25/2023

How many samples are needed to leverage smoothness?

A core principle in statistical learning is that smoothness of target fu...

Please sign up or login with your details

Forgot password? Click here to reset