gradient: Compute the gradient of a scalar-valued function
In nabla: Exact Derivatives via Automatic Differentiation
gradient R Documentation
Compute the gradient of a scalar-valued function
Description
Evaluates the gradient of f at x using forward-mode AD.
Equivalent to D(f, x) for scalar-valued f.
Usage
gradient(f, x)
Arguments
f
A function taking a parameter vector (numeric or dual) and
returning a scalar. Parameters are accessed via x[i].
x
A numeric vector of parameter values.
Details
The function should use x[1], x[2], etc. to access
parameters. This is compatible with the standard R convention used by
optim.
Value
A numeric vector of length p containing the gradient.
Examples
f <- function(x) -(x[1] - 3)^2 - (x[2] - 5)^2
gradient(f, c(1, 2))
nabla documentation built on Feb. 11, 2026, 1:06 a.m.
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| gradient | R Documentation |
Compute the gradient of a scalar-valued function
Description
Evaluates the gradient of f at x using forward-mode AD.
Equivalent to D(f, x) for scalar-valued f.
Usage
gradient(f, x)
Arguments
f |
A function taking a parameter vector (numeric or dual) and
returning a scalar. Parameters are accessed via |
x |
A numeric vector of parameter values. |
Details
The function should use x[1], x[2], etc. to access
parameters. This is compatible with the standard R convention used by
optim.
Value
A numeric vector of length p containing the gradient.
Examples
f <- function(x) -(x[1] - 3)^2 - (x[2] - 5)^2
gradient(f, c(1, 2))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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