generateGrid: Create a grid of points for a gradient / Jacobian

In pnd: Parallel Numerical Derivatives, Gradients, Jacobians, and Hessians of Arbitrary Accuracy Order

Create a grid of points for a gradient / Jacobian

Description

Create a grid of points for a gradient / Jacobian

Usage

generateGrid(x, h, stencils, elementwise, vectorised)

Arguments

x	Numeric vector or scalar: the point(s) at which the derivative is estimated. FUN(x) must be finite.
h	Numeric or character specifying the step size(s) for the numerical difference or a method of automatic step determination ("CR", "CRm", "DV", or "SW" to be used in gradstep()). The default value is described in ?GenD.
stencils	A list of outputs from fdCoef() for each coordinate of x.
elementwise	Logical: is the domain effectively 1D, i.e. is this a mapping \mathbb{R} \mapsto \mathbb{R} or \mathbb{R}^n \mapsto \mathbb{R}^n. If NA, compares the output length ot the input length.
vectorised	Logical: if TRUE, the function is assumed to be vectorised: it will accept a vector of parameters and return a vector of values of the same length. Use FALSE or "no" for functions that take vector arguments and return outputs of arbitrary length (for \mathbb{R}^n \mapsto \mathbb{R}^k functions). If NA, checks the output length and assumes vectorisation if it matches the input length; this check is necessary and potentially slow.

Value

A list with points for evaluation, summation weights for derivative computation, and indices for combining values.

Examples

generateGrid(1:4, h = 1e-5, elementwise = TRUE, vectorised = TRUE,
             stencils = lapply(1:4, function(a) fdCoef(acc.order = a)))

pnd documentation built on Sept. 9, 2025, 5:44 p.m.