fix indent

data/tetra.jl

@@ -1,16 +1,16 @@
 xe_tetra = [
-  0 0 0
-  1 0 0
-  0 1 0
-  0 0 1
-  5//10 2//10 1//10
+    0 0 0
+    1 0 0
+    0 1 0
+    0 0 1
+    5//10 2//10 1//10
 ];
 
 Tets_tetra = [
-  1 2 3 5
-  5 4 3 1
-  5 4 1 2
-  5 2 3 4
+    1 2 3 5
+    5 4 3 1
+    5 4 1 2
+    5 2 3 4
 ];
 
 Tets_tetra = vec(reshape(Tets_tetra, 16, 1));

data/triangle.jl

@@ -1,14 +1,14 @@
 xe = [
-  0 0
-  1 0
-  0 1
-  64//100 25//100
+    0 0
+    1 0
+    0 1
+    64//100 25//100
 ];
 
 Tr = [
-  1 2 4
-  4 2 3
-  4 3 1
+    1 2 4
+    4 2 3
+    4 3 1
 ];
 
 Tr = vec(reshape(Tr, 9, 1));

src/chain.jl

@@ -11,49 +11,54 @@
 # This file has been adapted from https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl
 
 function chain_model(n::Int)
-  nh = max(2, div(n - 4, 4))
-
-  L = 4
-  a = 1
-  b = 3
-  tmin = b > a ? 1 / 4 : 3 / 4
-  tf = 1.0
-  h = tf / nh
-
-  nlp = Model()
-
-  @variable(nlp, u[k = 1:(nh + 1)], start = 4 * abs(b - a) * (k / nh - tmin))
-  @variable(nlp, x1[k = 1:(nh + 1)], start = 4 * abs(b - a) * k / nh * (1 / 2 * k / nh - tmin) + a)
-  @variable(
-    nlp,
-    x2[k = 1:(nh + 1)],
-    start =
-      (4 * abs(b - a) * k / nh * (1 / 2 * k / nh - tmin) + a) * (4 * abs(b - a) * (k / nh - tmin))
-  )
-  @variable(nlp, x3[k = 1:(nh + 1)], start = 4 * abs(b - a) * (k / nh - tmin))
-
-  @objective(nlp, Min, x2[nh + 1])
-
-  for j = 1:nh
-    @constraint(nlp, x1[j + 1] - x1[j] - 1 / 2 * h * (u[j] + u[j + 1]) == 0)
-  end
-  @constraint(nlp, x1[1] == a)
-  @constraint(nlp, x1[nh + 1] == b)
-  @constraint(nlp, x2[1] == 0)
-  @constraint(nlp, x3[1] == 0)
-  @constraint(nlp, x3[nh + 1] == L)
-
-  @constraint(
-    nlp,
-    [j = 1:nh],
-    x2[j + 1] - x2[j] - 1 / 2 * h * (x1[j] * sqrt(1 + u[j]^2) + x1[j + 1] * sqrt(1 + u[j + 1]^2)) ==
-    0
-  )
-  @constraint(
-    nlp,
-    [j = 1:nh],
-    x3[j + 1] - x3[j] - 1 / 2 * h * (sqrt(1 + u[j]^2) + sqrt(1 + u[j + 1]^2)) == 0
-  )
-
-  return nlp
+    nh = max(2, div(n - 4, 4))
+
+    L = 4
+    a = 1
+    b = 3
+    tmin = b > a ? 1 / 4 : 3 / 4
+    tf = 1.0
+    h = tf / nh
+
+    nlp = Model()
+
+    @variable(nlp, u[k = 1:(nh + 1)], start = 4 * abs(b - a) * (k / nh - tmin))
+    @variable(
+        nlp,
+        x1[k = 1:(nh + 1)],
+        start = 4 * abs(b - a) * k / nh * (1 / 2 * k / nh - tmin) + a
+    )
+    @variable(
+        nlp,
+        x2[k = 1:(nh + 1)],
+        start =
+            (4 * abs(b - a) * k / nh * (1 / 2 * k / nh - tmin) + a) *
+            (4 * abs(b - a) * (k / nh - tmin))
+    )
+    @variable(nlp, x3[k = 1:(nh + 1)], start = 4 * abs(b - a) * (k / nh - tmin))
+
+    @objective(nlp, Min, x2[nh + 1])
+
+    for j = 1:nh
+        @constraint(nlp, x1[j + 1] - x1[j] - 1 / 2 * h * (u[j] + u[j + 1]) == 0)
+    end
+    @constraint(nlp, x1[1] == a)
+    @constraint(nlp, x1[nh + 1] == b)
+    @constraint(nlp, x2[1] == 0)
+    @constraint(nlp, x3[1] == 0)
+    @constraint(nlp, x3[nh + 1] == L)
+
+    @constraint(
+        nlp,
+        [j = 1:nh],
+        x2[j + 1] - x2[j] -
+        1 / 2 * h * (x1[j] * sqrt(1 + u[j]^2) + x1[j + 1] * sqrt(1 + u[j + 1]^2)) == 0
+    )
+    @constraint(
+        nlp,
+        [j = 1:nh],
+        x3[j + 1] - x3[j] - 1 / 2 * h * (sqrt(1 + u[j]^2) + sqrt(1 + u[j + 1]^2)) == 0
+    )
+
+    return nlp
 end

src/minsurf.jl

@@ -12,46 +12,48 @@
 # This file has been adapted from https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl
 
 function minsurf_model(nx::Int, ny::Int)
-  x_mesh = LinRange(0, 1, nx + 2) # coordinates of the mesh points x
-
-  v0 = zeros(nx + 2, ny + 2) # Surface matrix initialization
-  for i = 1:(nx + 2), j = 1:(ny + 2)
-    v0[i, j] = 1 - (2 * x_mesh[i] - 1)^2
-  end
-
-  hx = 1 / (nx + 1)
-  hy = 1 / (ny + 1)
-  area = 1 // 2 * hx * hy
-
-  nlp = Model()
-
-  @variable(nlp, v[i = 1:(nx + 2), j = 1:(ny + 2)], start = v0[i, j])
-
-  @objective(
-    nlp,
-    Min,
-    sum(
-      area * (1 + ((v[i + 1, j] - v[i, j]) / hx)^2 + ((v[i, j + 1] - v[i, j]) / hy)^2)^(1 / 2) for
-      i = 1:(nx + 1), j = 1:(ny + 1)
-    ) + sum(
-      area * (1 + ((v[i - 1, j] - v[i, j]) / hx)^2 + ((v[i, j - 1] - v[i, j]) / hy)^2)^(1 / 2) for
-      i = 2:(nx + 2), j = 2:(ny + 2)
+    x_mesh = LinRange(0, 1, nx + 2) # coordinates of the mesh points x
+
+    v0 = zeros(nx + 2, ny + 2) # Surface matrix initialization
+    for i = 1:(nx + 2), j = 1:(ny + 2)
+        v0[i, j] = 1 - (2 * x_mesh[i] - 1)^2
+    end
+
+    hx = 1 / (nx + 1)
+    hy = 1 / (ny + 1)
+    area = 1 // 2 * hx * hy
+
+    nlp = Model()
+
+    @variable(nlp, v[i = 1:(nx + 2), j = 1:(ny + 2)], start = v0[i, j])
+
+    @objective(
+        nlp,
+        Min,
+        sum(
+            area *
+            (1 + ((v[i + 1, j] - v[i, j]) / hx)^2 + ((v[i, j + 1] - v[i, j]) / hy)^2)^(1 / 2) for
+            i = 1:(nx + 1), j = 1:(ny + 1)
+        ) + sum(
+            area *
+            (1 + ((v[i - 1, j] - v[i, j]) / hx)^2 + ((v[i, j - 1] - v[i, j]) / hy)^2)^(1 / 2) for
+            i = 2:(nx + 2), j = 2:(ny + 2)
+        )
     )
-  )
-
-  @constraint(nlp, [j = 0:(ny + 1)], v[1, j + 1] == 0)
-  @constraint(nlp, [j = 0:(ny + 1)], v[nx + 2, j + 1] == 0)
-  @constraint(nlp, [i = 0:(nx + 1)], v[i + 1, 1] == 1 - (2 * i * hx - 1)^2)
-  @constraint(nlp, [i = 0:(nx + 1)], v[i + 1, ny + 2] == 1 - (2 * i * hx - 1)^2)
-  @constraint(nlp, [i = 0:(nx + 1), j = 0:(ny + 1)], v[i + 1, j + 1] >= 0)
-  @constraint(
-    nlp,
-    [
-      i = Int(floor(0.25 / hx)):Int(ceil(0.75 / hx)),
-      j = Int(floor(0.25 / hy)):Int(ceil(0.75 / hy)),
-    ],
-    v[i + 1, j + 1] >= 1
-  )
-
-  return nlp
+
+    @constraint(nlp, [j = 0:(ny + 1)], v[1, j + 1] == 0)
+    @constraint(nlp, [j = 0:(ny + 1)], v[nx + 2, j + 1] == 0)
+    @constraint(nlp, [i = 0:(nx + 1)], v[i + 1, 1] == 1 - (2 * i * hx - 1)^2)
+    @constraint(nlp, [i = 0:(nx + 1)], v[i + 1, ny + 2] == 1 - (2 * i * hx - 1)^2)
+    @constraint(nlp, [i = 0:(nx + 1), j = 0:(ny + 1)], v[i + 1, j + 1] >= 0)
+    @constraint(
+        nlp,
+        [
+            i = Int(floor(0.25 / hx)):Int(ceil(0.75 / hx)),
+            j = Int(floor(0.25 / hy)):Int(ceil(0.75 / hy)),
+        ],
+        v[i + 1, j + 1] >= 1
+    )
+
+    return nlp
 end

src/polygon.jl

@@ -9,23 +9,23 @@
 # This file has been adapted from https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl
 
 function polygon_model(n::Int)
-  nlp = Model()
-  N = div(n, 2)
-  @variable(nlp, 0 <= r[1:N] <= 1, start = 1)
-  @variable(nlp, 0 <= θ[i = 1:N] <= π, start = i * π / (N - 1) - π / (N - 1))
+    nlp = Model()
+    N = div(n, 2)
+    @variable(nlp, 0 <= r[1:N] <= 1, start = 1)
+    @variable(nlp, 0 <= θ[i = 1:N] <= π, start = i * π / (N - 1) - π / (N - 1))
 
-  # impose an order to the angles
-  @constraint(nlp, θ[N] == π)
-  @constraint(nlp, r[N] == 0)
-  for i = 1:(N - 1)
-    @constraint(nlp, θ[i + 1] - θ[i] >= 0.0)
-  end
-  for i = 1:(N - 1)
-    for j = (i + 1):N
-      @constraint(nlp, r[i]^2 + r[j]^2 - 2 * r[i] * r[j] * cos(θ[i] - θ[j]) - 1 <= 0)
+    # impose an order to the angles
+    @constraint(nlp, θ[N] == π)
+    @constraint(nlp, r[N] == 0)
+    for i = 1:(N - 1)
+        @constraint(nlp, θ[i + 1] - θ[i] >= 0.0)
+    end
+    for i = 1:(N - 1)
+        for j = (i + 1):N
+            @constraint(nlp, r[i]^2 + r[j]^2 - 2 * r[i] * r[j] * cos(θ[i] - θ[j]) - 1 <= 0)
+        end
     end
-  end
 
-  @objective(nlp, Min, -0.5 * sum(r[i] * r[i + 1] * sin(θ[i + 1] - θ[i]) for i = 1:(N - 1)))
-  return nlp
+    @objective(nlp, Min, -0.5 * sum(r[i] * r[i + 1] * sin(θ[i + 1] - θ[i]) for i = 1:(N - 1)))
+    return nlp
 end