🤖 Format .jl files (#395)

Co-authored-by: tmigot <[email protected]>

Author: github-actions[bot]

src/ADNLPProblems/variational.jl

@@ -4,28 +4,28 @@ function variational(; n::Int = default_nvar, type::Type{T} = Float64, kwargs...
   h = 1 // (n + 1)
   x0 = T[(i * h) * (1 - i * h) for i = 1:n]
 
-function f(x; n = length(x))
-  Ti = eltype(x)
+  function f(x; n = length(x))
+    Ti = eltype(x)
 
-  term1 = zero(Ti)
-  term2 = zero(Ti)
-  @inbounds for k = 1:n
-    xi  = x[k]
-    xip = (k < n) ? x[k + 1] : zero(Ti)
+    term1 = zero(Ti)
+    term2 = zero(Ti)
+    @inbounds for k = 1:n
+      xi = x[k]
+      xip = (k < n) ? x[k + 1] : zero(Ti)
 
-    term1 += xi * (xi - xip) / h
+      term1 += xi * (xi - xip) / h
 
-    a_prev = (k == 1) ? zero(Ti) : x[k - 1]
-    b_prev = xi
-    term2 += (exp(b_prev) - exp(a_prev)) / (b_prev - a_prev)
+      a_prev = (k == 1) ? zero(Ti) : x[k - 1]
+      b_prev = xi
+      term2 += (exp(b_prev) - exp(a_prev)) / (b_prev - a_prev)
 
-    if k == n
-      a_last, b_last = xi, zero(Ti)
-      term2 += (exp(b_last) - exp(a_last)) / (b_last - a_last)
+      if k == n
+        a_last, b_last = xi, zero(Ti)
+        term2 += (exp(b_last) - exp(a_last)) / (b_last - a_last)
+      end
     end
+    return 2 * (term1 + 2 * h * term2)
   end
-  return 2 * (term1 + 2 * h * term2)
-end
 
   return ADNLPModels.ADNLPModel(f, x0, name = "variational"; kwargs...)
 end

src/PureJuMP/variational.jl

@@ -21,7 +21,8 @@ function variational(; n::Int = default_nvar, kwargs...)
     Min,
     2 * (
       (sum(x[i] * (x[i] - x[i + 1]) / h for i ∈ 1:(n - 1)) + x[n] * x[n] / h) +
-      2 * h *
+      2 *
+      h *
       (
         (exp(x[1]) - exp(0)) / (x[1] - 0) +
         sum((exp(x[j + 1]) - exp(x[j])) / (x[j + 1] - x[j]) for j ∈ 1:(n - 1)) +