checkCores |
Number of core checks and changes |
checkDimensions |
Determine function dimensionality and vectorisation |
dupRowInds |
Repeated indices of the first unique value |
fdCoef |
Finite-difference coefficients for arbitrary grids |
GenD |
Numerical derivative matrices with parallel capabilities |
generateGrid |
Create a grid of points for a gradient / Jacobian |
generateGrid2 |
Generate grid points for Hessians |
Grad |
Gradient computation with parallel capabilities |
gradstep |
Automatic step selection for numerical derivatives |
Hessian |
Numerical Hessians |
Jacobian |
Jacobian matrix computation with parallel capabilities s Computes the numerical Jacobian for vector-valued functions. Its columns are partial derivatives of the function with respect to the input elements. This function supports both two-sided (central, symmetric) and one-sided (forward or backward) derivatives. It can utilise parallel processing to accelerate computation of gradients for slow functions or to attain higher accuracy faster. Currently, only Mac and Linux are supported 'parallel::mclapply()'. Windows support with 'parallel::parLapply()' is under development. |
plotTE |
Estimated total error plot as in Mathur (2012) |
runParallel |
Run a function in parallel over a list (internal use only) |
solveVandermonde |
Numerically stable non-confluent Vandermonde system solver |
step.CR |
Curtis-Reid automatic step selection |
step.DV |
Dumontet-Vignes automatic step selection |
step.M |
Mathur's AutoDX-like automatic step selection |
step.plugin |
Plug-in step selection |
step.SW |
Stepleman-Winarsky automatic step selection |
stepx |
Default step size at given points |