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Source file: forward-ad.fut

Forward-mode automatic differentiation

The built-in jvp function computes the product of a seed vector and the Jacobian of a function at some point. This can be used to compute derivatives of functions.

def f x = f64.sqrt(x) * f64.sin(x)

def f' x = jvp f x 1
> f' 2.0f64

jvp corresponds to differentiation by forward accumulation, and is most efficient for functions that have more outputs than inputs. For a function with multiple inputs, you need multiple applications of f' to compute the full derivative, each with a one-hot seed vector:

def g (x,y) = f64.cos(x) * f64.sin(y)

def g' x y = (jvp g (x,y) (1,0), jvp g (x,y) (0,1))
> g' 1.0f64 2.0f64
(-0.7651474012342926f64, -0.2248450953661529f64)

See also

Reverse-mode automatic differentiation.