Algorithm 1 An Iterative Linear Programming Approach [53]
Input: functions f,g ∈ Lp(Ω), measures μ,ν ∈ 𝒫(Ω) and parameters h0, N.
1:	Set r = 0.
2:	repeat
3:	Define ${\mathcal{S}}_r={\mathrm{\Omega }}_{hr}^2$ where Ωhr is the square grid lattice with distances between neighbouring points hr and discretise functions f, g and measures μ,ν on Ωh.
4:	if r = 0 then
5:	Solve (LPh) on So and call the output πh0.
6:	else
7:	Find the set of nodes on 𝒮r−1 for which πhr−1 is non-zero and call the set 𝒦r−1.
8:	To 𝒦r−1 add all neighbouring nodes and call this set 𝒩r−1.
9:	Define ℳr to be the set of nodes on 𝒮r that are children of nodes in 𝒩r−1.
10:	Solve (LPh) restricted to ℳr and call the optimal plan πhr.
11:	end if
12:	Set $h_{r+1}=\frac{h_r}{2}$ and r ↦ r +1.
13:	until r = N
Output: The optimal plan πhN−1 for (LPh).