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# Copyright (C) 2011 Jelmer Ypma. All Rights Reserved.
# SPDX-License-Identifier: LGPL-3.0-or-later
#
# File: finite.diff.R
# Author: Jelmer Ypma
# Date: 24 July 2011
#
# CHANGELOG
#
# 2023-02-10: Tweaked for efficiency and clarity. Also switched to using vapply
# which is both faster and type-safe. Not sure if we need to carry
# both this and nl.grad/jacobian but not changing for now.
# (Avraham Adler)
#
# Approximate the gradient of a function using finite differences.
#
# Input:
# func : calculate finite difference approximation for the gradient of
# this function
# .x : evaluate at this point
# indices : calculate finite difference approximation only for .x-indices
# in this vector (optional)
# stepSize : relative size of the step between x and x + h (optional)
# ... : arguments that are passed to the user-defined function (func)
#
# Output: matrix with finite difference approximations
#
# http://en.wikipedia.org/wiki/Numerical_differentiation
finite.diff <- function(func, .x, indices = seq_along(.x),
stepSize = sqrt(.Machine$double.eps), ...) {
# if we evaluate at values close to 0, we need a different step size
stepSizeVec <- pmax(abs(.x), 1) * stepSize
fx <- func(.x, ...)
approx.gradf.index <- function(i, .x, func, fx, stepSizeVec, ...) {
x_prime <- .x
x_prime[i] <- .x[i] + stepSizeVec[i]
stepSizeVec[i] <- x_prime[i] - .x[i]
fx_prime <- func(x_prime, ...)
(fx_prime - fx) / stepSizeVec[i]
}
vapply(indices, approx.gradf.index, double(length(fx)), .x = .x, func = func,
fx = fx, stepSizeVec = stepSizeVec, ...)
}
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