`external central : f:(float -> float) -> x:float -> h:float -> Fun.result = "ml_gsl_deriv_central" `

`central f x h`

computes the numerical derivative of the function `f`

at the point `x`

using an adaptive central difference algorithm with a step-size of `h`

. The function returns a value `r`

with the derivative being in `r.res`

and an estimate of its absolute error in `r.err`

.

`external forward : f:(float -> float) -> x:float -> h:float -> Fun.result = "ml_gsl_deriv_forward" `

`forward f x h`

computes the numerical derivative of the function `f`

at the point `x`

using an adaptive forward difference algorithm with a step-size of `h`

. The function is evaluated only at points greater than `x`

, and never at `x`

itself. The function returns `r`

with the derivative in `r.res`

and an estimate of its absolute in `r.err`

. This function should be used if f(x) has a discontinuity at `x`

, or is undefined for values less than `x`

.

`external backward : f:(float -> float) -> x:float -> h:float -> Fun.result = "ml_gsl_deriv_backward" `

`forward f x h`

computes the numerical derivative of the function `f`

at the point `x`

using an adaptive backward difference algorithm with a step-size of `h`

. The function is evaluated only at points less than `x`

, and never at `x`

itself. The function returns a value `r`

with the derivative in `r.res`

and an estimate of its absolute error in `r.err`

. This function should be used if f(x) has a discontinuity at `x`

, or is undefined for values greater than `x`

.