I have an algorithm that generates a tree by multiplying two functions. It works, but my goal is to make it faster. With profiling I’ve found that a substantial amount of time is spend on sorting.

I’m wondering if the sorting would be faster if trees were represented by a data structure different than vectors of any types, and if so, how to represent a tree as such structure (I tried but failed so far and don’t even know if it’s worth the hassle ).

A part of the code that appears to be taking the most time (especially for larger inputs) with necessary minimal working additions and example input is as follows:

```
f1 = Vector{Vector{Any}}[[[0, Any[0, Any[0, [0, 0]]]]], [[100800, Any[0, Any[0, [0, 0]]]]], [[201600, Any[0, Any[0, [0, 0]]]]], [[302400, Any[0, Any[0, [0, 0]]]]], [[403200, Any[0, Any[0, [0, 0]]]]], [[504000, Any[0, Any[0, [0, 0]]]]], [[604800, Any[0, Any[0, [0, 0]]]]], [[705600, Any[0, Any[0, [0, 0]]]]], [[806400, Any[0, Any[0, [0, 0]]]]], [[907200, Any[0, Any[0, [0, 0]]]]], [[1008000, Any[0, Any[0, [0, 0]]]]], [[1108800, Any[0, Any[0, [0, 0]]]]], [[1209600, Any[0, Any[0, [0, 0]]]]], [[1310400, Any[0, Any[0, [0, 0]]]]], [[0, Any[1400700, Any[0, [0, 0]]]]]]
f2 = [0, 1201100]
M = 1402700
function is_leaf(t)
return length(t) == 1
end
function sum_tree(t)
return is_leaf(t) ? sum(t) : t[1] + sum_tree(t[2])
end
function multiply_gf(f1, f2)
r = collect((vcat(j,i) for i in f1 for j in f2 if sum_tree(vcat(j,i)) <= M))
sort!(r, by = sum_tree)
println(r)
end
multiply_gf(f1, f2)
```

As you can see, f1 is Vector{Vector{Any}} just like the result r.