# Illustration how to use mutating gradient functions

When it comes to time critital operations, a main ingredient in Julia are mutating functions, i.e. those that compute in place without additional Memory allocations. In the following the illustrate how to do this with `Manopt.jl`

.

Let's start with the same function as in Get Started: Optimize! and compute the mean of some points. Just that here we use the sphere $\mathbb S^{30}$ and `n=800`

points.

From the just mentioned example, the implementation looks like

```
using Manopt, Manifolds, Random, BenchmarkTools, Test
Random.seed!(42)
m = 30
M = Sphere(m)
n = 800
σ = π / 8
x = zeros(Float64, m + 1)
x[2] = 1.0
data = [exp(M, x, random_tangent(M, x, Val(:Gaussian), σ)) for i in 1:n];
```

## Classical definition

The variant from the previous tutorial defines a cost $F(x)$ and its gradient $gradF(x)$

```
F(x) = sum(1 / (2 * n) * distance.(Ref(M), Ref(x), data) .^ 2)
gradF(M, x) = sum(1 / n * grad_distance.(Ref(M), data, Ref(x)))
```

we further set the stopping criterion to be a little more strict, then we obtain

```
sc = StopWhenGradientNormLess(1e-10)
x0 = random_point(M)
m1 = gradient_descent(M, F, gradF, x0; stopping_criterion=sc)
@btime gradient_descent($M, $F, $gradF, $x0; stopping_criterion=$sc)
```

` 13.577 ms (86711 allocations: 28.03 MiB)`

## Inplace computation of the gradient

We can reduce the memory allocations, by implementing the gradient as a functor. The motivation is twofold: On the one hand, we want to avoid variables from global scope, for example the manifold `M`

or the `data`

, to be used within the function For more complicated cost functions it might also be worth considering to do the same.

Here we store the data (as reference) and one temporary memory in order to avoid reallocation of memory per `grad_distance`

computation. We have

```
struct grad!{TD,TTMP}
data::TD
tmp::TTMP
end
function (gradf!::grad!)(M, X, x)
fill!(X, 0)
for di in gradf!.data
grad_distance!(M, gradf!.tmp, di, x)
X .+= gradf!.tmp
end
X ./= length(gradf!.data)
return X
end
```

Then we just have to initialize the gradient and perform our final benchmark. Note that we also have to interpolate all variables passed to the benchmark with a `$`

.

```
gradF2! = grad!(data, similar(data[1]))
m2 = deepcopy(x0)
gradient_descent!(M, F, gradF2!, m2; evaluation=MutatingEvaluation(), stopping_criterion=sc)
@btime gradient_descent!(
$M, $F, $gradF2!, m2; evaluation=$(MutatingEvaluation()), stopping_criterion=$sc
) setup = (m2 = deepcopy($x0))
```

` 6.749 ms (96 allocations: 11.78 KiB)`

Mote that the results `m1`

and `m2`

are of course the same.

`@test distance(M, m1, m2) ≈ 0`