🤖 Format .jl files

JuliaSmoothOptimizers · Jul 2, 2021 · b57e37c · b57e37c · abelsiqueira · Jul 2, 2021
1 parent 2ba6bad
commit b57e37c
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Showing 3 changed files with 30 additions and 9 deletions.
diff --git a/src/nls/problems/lls.jl b/src/nls/problems/lls.jl
@@ -184,7 +184,12 @@ function NLPModels.hess_structure!(nls::LLS, rows::AbstractVector{Int}, cols::Ab
   return rows, cols
 end
 
-function NLPModels.hess_coord!(nls::LLS{T, S}, x::AbstractVector{T}, vals::AbstractVector; obj_weight :: T = one(T)) where {T, S}
+function NLPModels.hess_coord!(
+  nls::LLS{T, S},
+  x::AbstractVector{T},
+  vals::AbstractVector;
+  obj_weight::T = one(T),
+) where {T, S}
   @lencheck 2 x
   @lencheck 2 vals
   vals .= [2obj_weight; 3obj_weight]

diff --git a/src/nls/problems/nlshs20.jl b/src/nls/problems/nlshs20.jl
@@ -183,17 +183,28 @@ function NLPModels.jtprod!(nls::NLSHS20, x::AbstractVector, v::AbstractVector, J
   return Jtv
 end
 
-function NLPModels.hess(nls :: NLSHS20, x :: AbstractVector{T}; obj_weight=1.0) where T
+function NLPModels.hess(nls::NLSHS20, x::AbstractVector{T}; obj_weight = 1.0) where {T}
   @lencheck 2 x
   increment!(nls, :neval_hess)
-  return Symmetric(obj_weight * [T(1)-200*x[2]+600*x[1]^2 T(0);-200*x[1] T(100)], :L)
+  return Symmetric(obj_weight * [T(1) - 200 * x[2]+600 * x[1]^2 T(0); -200*x[1] T(100)], :L)
 end
 
-function NLPModels.hess(nls :: NLSHS20, x :: AbstractVector{T}, y :: AbstractVector{T}; obj_weight=1.0) where T
+function NLPModels.hess(
+  nls::NLSHS20,
+  x::AbstractVector{T},
+  y::AbstractVector{T};
+  obj_weight = 1.0,
+) where {T}
   @lencheck 2 x
   @lencheck 3 y
   increment!(nls, :neval_hess)
-  return Symmetric([obj_weight*(T(1)-200*x[2]+600*x[1]^2)+2*y[2]+2*y[3] T(0);-obj_weight*200*x[1] obj_weight*T(100)+2*y[1]+2*y[3]], :L)
+  return Symmetric(
+    [
+      obj_weight*(T(1) - 200 * x[2] + 600 * x[1]^2)+2*y[2]+2*y[3] T(0)
+      -obj_weight*200*x[1] obj_weight*T(100)+2*y[1]+2*y[3]
+    ],
+    :L,
+  )
 end
 
 function NLPModels.hess_structure!(

diff --git a/src/nls/problems/nlslc.jl b/src/nls/problems/nlslc.jl
@@ -216,17 +216,22 @@ function NLPModels.jtprod!(nls::NLSLC, x::AbstractVector, v::AbstractVector, Jtv
   return Jtv
 end
 
-function NLPModels.hess(nls :: NLSLC, x :: AbstractVector{T}; obj_weight=1.0) where T
+function NLPModels.hess(nls::NLSLC, x::AbstractVector{T}; obj_weight = 1.0) where {T}
   @lencheck 15 x
   increment!(nls, :neval_hess)
-  return Symmetric(obj_weight * diagm(0 => [6*x[i]^2-2*i^2 for i=1:15]), :L)
+  return Symmetric(obj_weight * diagm(0 => [6 * x[i]^2 - 2 * i^2 for i = 1:15]), :L)
 end
 
-function NLPModels.hess(nls :: NLSLC, x :: AbstractVector{T}, y :: AbstractVector{T}; obj_weight=1.0) where T
+function NLPModels.hess(
+  nls::NLSLC,
+  x::AbstractVector{T},
+  y::AbstractVector{T};
+  obj_weight = 1.0,
+) where {T}
   @lencheck 15 x
   @lencheck 11 y
   increment!(nls, :neval_hess)
-  return Symmetric(hess(nls, x, obj_weight=obj_weight), :L)
+  return Symmetric(hess(nls, x, obj_weight = obj_weight), :L)
 end
 
 function NLPModels.hess_structure!(nls::NLSLC, rows::AbstractVector{Int}, cols::AbstractVector{Int})