R: Examples for 'stats::nlm'

### ** Examples

f <- function(x) sum((x-1:length(x))^2)
nlm(f, c(10,10))

$minimum
[1] 4.303458e-26

$estimate
[1] 1 2

$gradient
[1]  2.757794e-13 -3.099743e-13

$code
[1] 1

$iterations
[1] 2

nlm(f, c(10,10), print.level = 2)

iteration = 0
Step:
[1] 0 0
Parameter:
[1] 10 10
Function Value
[1] 145
Gradient:
[1] 18.00001 16.00001

iteration = 1
Step:
[1] -9 -8
Parameter:
[1] 1 2
Function Value
[1] 1.721748e-13
Gradient:
[1] 1.551336e-06 1.379735e-06

iteration = 2
Parameter:
[1] 1 2
Function Value
[1] 4.303458e-26
Gradient:
[1]  2.757794e-13 -3.099743e-13

Relative gradient close to zero.
Current iterate is probably solution.

$minimum
[1] 4.303458e-26

$estimate
[1] 1 2

$gradient
[1]  2.757794e-13 -3.099743e-13

$code
[1] 1

$iterations
[1] 2

utils::str(nlm(f, c(5), hessian = TRUE))

List of 6
 $ minimum   : num 2.44e-24
 $ estimate  : num 1
 $ gradient  : num 1e-06
 $ hessian   : num [1, 1] 2
 $ code      : int 1
 $ iterations: int 1

f <- function(x, a) sum((x-a)^2)
nlm(f, c(10,10), a = c(3,5))

$minimum
[1] 3.371781e-25

$estimate
[1] 3 5

$gradient
[1]  6.750156e-13 -9.450218e-13

$code
[1] 1

$iterations
[1] 2

f <- function(x, a)
{
    res <- sum((x-a)^2)
    attr(res, "gradient") <- 2*(x-a)
    res
}
nlm(f, c(10,10), a = c(3,5))

$minimum
[1] 0

$estimate
[1] 3 5

$gradient
[1] 0 0

$code
[1] 1

$iterations
[1] 1

## more examples, including the use of derivatives.
## Not run: demo(nlm)

Examples for 'stats::nlm'

Non-Linear Minimization