# Illustration of the Gradient of a Second Order Difference

This example explains how to compute the gradient of the second order difference mid point model using `adjointJacobiField`

s.

This example also illustrates the `Power`

manifold as well as `ArmijoLinesearch`

.

We first initialize the manifold

`using Manopt`

and we define some colors from Paul Tol

```
using Colors
black = RGBA{Float64}(colorant"#000000")
TolVibrantBlue = RGBA{Float64}(colorant"#0077BB") # points
TolVibrantOrange = RGBA{Float64}(colorant"#EE7733") # results
TolVibrantCyan = RGBA{Float64}(colorant"#33BBEE") # vectors
TolVibrantTeal = RGBA{Float64}(colorant"#009988") # geo
asyResolution = 2
```

Assume we have two `SnPoint`

s $x,y$ on the equator of the `Sphere`

`(2)`

$\mathcal M = \mathbb S^2$ and a point $y$ near the north pole

```
M = Sphere(2)
x = SnPoint([1., 0., 0.])
z = SnPoint([0., 1., 0.])
c = midPoint(M,x,z)
y = geodesic(M, SnPoint([0., 0., 1.]), c, 0.1)
[c,y]
```

```
2-element Array{SnPoint{Float64},1}:
Sn([0.707107, 0.707107, 0.0])
Sn([0.110616, 0.110616, 0.987688])
```

Now the second order absolute difference can be stated as (see [Bačák, Bergmann, Steidl, Weinmann, 2016])

where $\mathcal C_{x,z}$ is the set of all mid points $g(\frac{1}{2};x,z)$, where $g$ is a (not necessarily minimizing) geodesic connecting $x$ and $z$.

For illustration we further define the point opposite of

`c2 = opposite(M,c)`

`Sn([-0.707107, -0.707107, -0.0])`

and draw the geodesic connecting $y$ and the nearest mid point $c$, namely

```
T = [0:0.1:1.0...]
geoPts_yc = geodesic(M,y,c,T)
```

looks as follows using `renderAsymptote`

with the `asyExportS2Signals`

export

```
renderAsymptote("secondOrderData.asy",asyExportS2Signals;
render = asyResolution,
curves = [ geoPts_yc ],
points = [ [x,y,z], [c,c2] ],
colors=Dict(:curves => [TolVibrantTeal], :points => [black, TolVibrantBlue]),
dotSize = 3.5, lineWidth = 0.75, cameraPosition = (1.2,1.,.5)
)
```

Since we moved $y$ 10% along the geodesic from the north pole to $c$, the distance to $c$ is $\frac{9\pi}{20}\approx 1.4137$, and this is also what

`costTV2(M, (x,y,z) )`

`1.413716694115407`

returns, see `costTV2`

for reference. But also its gradient can be easily computed since it is just a distance with respect to $y$ and a concatenation of a geodesic, where the start or end point is the argument, respectively, with a distance. Hence the adjoint differentials `AdjDxGeo`

and `AdjDyGeo`

can be employed, see `gradTV2`

for details. we obtain

`(ξx, ξy, ξz) = gradTV2(M, (x,y,z) )`

`(SnT([-0.0, -4.9677e-18, -0.707107]), SnT([-0.698401, -0.698401, 0.156434]), SnT([4.9677e-18, 0.0, -0.707107]))`

When we aim to minimize this, we look at the negative gradient, i.e. we can draw this as

```
renderAsymptote("SecondOrderGradient.asy",asyExportS2Signals;
render = asyResolution,
points = [ [x,y,z], [c,c2] ],
tVectors = [TVectorE.( [-ξx, -ξy, -ξz], [x, y, z] )],
colors=Dict(:tvectors => [TolVibrantCyan], :points => [black, TolVibrantBlue]),
dotSize = 3.5, lineWidth = 0.75, cameraPosition = (1.2,1.,.5)
)
```

If we now perform a gradient step, we obtain the three points

`xn, yn, zn = exp.(Ref(M), [x,y,z], [-ξx,-ξy,-ξz])`

```
3-element Array{SnPoint{Float64},1}:
Sn([0.760245, 4.56395e-18, 0.649637])
Sn([0.64745, 0.64745, 0.402015])
Sn([-4.56395e-18, 0.760245, 0.649637])
```

as well we the new mid point

```
cn = midPoint(M,xn,zn)
geoPts_yncn = geodesic(M,yn,cn,T)
```

and obtain the new situation

```
renderAsymptote("SecondOrderMin1.asy",asyExportS2Signals;
render = asyResolution,
points = [ [x,y,z], [c,c2,cn], [xn,yn,zn] ],
curves = [ geoPts_yncn ] ,
tVectors = [TVectorE.( [-ξx, -ξy, -ξz], [x, y, z] )],
colors=Dict(:tvectors => [TolVibrantCyan],
:points => [black, TolVibrantBlue, TolVibrantOrange],
:curves => [TolVibrantTeal]
),
dotSize = 3.5, lineWidth = 0.75, cameraPosition = (1.2,1.,.5)
)
```

One can see, that this step slightly “overshoots”, i.e. $y$ is now even below $c$. and the cost function is still at

`costTV2(M, (xn, yn, zn) )`

`0.4657942881828864`

But we can also search for the best step size using `ArmijoLinesearch`

on the `Power`

manifold $\mathcal N = \mathcal M^3 = (\mathbb S^2)^3$

```
p = PowPoint([x,y,z])
N = Power(M,3)
s = ArmijoLinesearch(1.0,exp,0.999,0.96)(N, p,
x -> costTV2(M, Tuple(getValue(x))),
PowTVector( [ gradTV2(M, (x,y,z))... ] ) # transform from tuple to PowTVector
)
```

`0.7348020742679654`

and for the new points

```
xm, ym, zm = exp.(Ref(M), [x,y,z], s*[-ξx,-ξy,-ξz])
cm = midPoint(M,xm,zm)
geoPts_xmzm = geodesic(M,xm,zm,T)
```

we obtain again with

```
renderAsymptote("SecondOrderMin2.asy",asyExportS2Signals;
render = asyResolution,
points = [ [x,y,z], [c,c2,cm], [xm,ym,zm] ],
curves = [ geoPts_xmzm ] ,
tVectors = [TVectorE.( [-ξx, -ξy, -ξz], [x, y, z] )],
colors=Dict(:tvectors => [TolVibrantCyan],
:points => [black, TolVibrantBlue, TolVibrantOrange],
:curves => [TolVibrantTeal]
),
dotSize = 3.5, lineWidth = 0.75, cameraPosition = (1.2,1.,.5)
)
```

Here, the cost function yields

`costTV2( M, (xm, ym, zm) )`

`0.0012555303253956782`

which is nearly zero, as one can also see, since the new center $c$ and $y$ are quite close.

## Literature

- [Bačák, Bergmann, Steidl, Weinmann, 2016]
Bačák, M; Bergmann, R.; Steidl, G; Weinmann, A.:
A second order nonsmooth variational model for restoring manifold-valued images. , SIAM Journal on Scientific Computations, Volume 38, Number 1, pp. A567–597, doi: 10.1137/15M101988X