Hi!

I’m writing a function that constructs a symmetric sparse matrix. I’ve tried two implementations:

```
function version1(x::Vector{Date}, k::Function)
n = length(x)
mat = speye(Float64, n, n)
for i = 1:n
for j = 1:(i - 1)
w = Dates.value(x[i] - x[j])
w = k(w)
(w > 0.0) && (mat[j, i] = w)
end
end
return Symmetric(mat)
end
function version2(x::Vector{Date}, k::Function)
n = length(x)
idx₁ = Vector{Int}(1:n)
idx₂ = Vector{Int}(1:n)
val = fill(1.0, n)
for i = 1:n
for j = 1:(i - 1)
w = Dates.value(x[i] - x[j])
w = k(w)
if w > 0.0
push!(idx₁, j)
push!(idx₂, i)
push!(val, w)
end
end
end
return Symmetric(sparse(idx₁, idx₂, val))
end
```

I’ve tried them on both on a vector of 5400 dates. After an initial run for compilation, `@time`

gives: version1: 7.849785 seconds (48 allocations: 83.900 MiB); version2: 0.676957 seconds (75 allocations: 283.136 MiB, 24.15% gc time). These results are consistent across multiple runs.

Is there are a reason to prefer `version1`

? Are the memory savings worth the extra running time?