🤖 Format .jl files

Author: dpo

src/linalg_utils.jl

@@ -7,4 +7,4 @@ function SparseArrays.nnz(M::Symmetric{T, <:DenseMatrix{T}}) where {T}
   n = size(M, 1)
   return n * (n + 1) / 2
 end
-SparseArrays.nnz(M::Symmetric{T, <:AbstractSparseMatrix{T}}) where {T} = nnz(M.data)
+SparseArrays.nnz(M::Symmetric{T, <:AbstractSparseMatrix{T}}) where {T} = nnz(M.data)

src/qpmodel.jl

@@ -14,12 +14,18 @@ end
 
 isdense(data::QPData{T, S, M1, M2}) where {T, S, M1, M2} = M1 <: DenseMatrix || M2 <: DenseMatrix
 
-function Base.convert(::Type{QPData{T, S, MCOO, MCOO}}, data::QPData{T, S, M1, M2}) where {T, S, M1 <: AbstractMatrix, M2 <: AbstractMatrix, MCOO <: SparseMatrixCOO{T}}
+function Base.convert(
+  ::Type{QPData{T, S, MCOO, MCOO}},
+  data::QPData{T, S, M1, M2},
+) where {T, S, M1 <: AbstractMatrix, M2 <: AbstractMatrix, MCOO <: SparseMatrixCOO{T}}
   HCOO = (M1 <: SparseMatrixCOO) ? data.H : SparseMatrixCOO(data.H)
   ACOO = (M2 <: SparseMatrixCOO) ? data.A : SparseMatrixCOO(data.A)
   return QPData(data.c0, data.c, HCOO, ACOO)
 end
-Base.convert(::Type{QPData{T, S, MCOO, MCOO}}, data::QPData{T, S, M1, M2}) where {T, S, M1 <: SparseMatrixCOO, M2 <: SparseMatrixCOO, MCOO <: SparseMatrixCOO{T}} = data
+Base.convert(
+  ::Type{QPData{T, S, MCOO, MCOO}},
+  data::QPData{T, S, M1, M2},
+) where {T, S, M1 <: SparseMatrixCOO, M2 <: SparseMatrixCOO, MCOO <: SparseMatrixCOO{T}} = data
 
 abstract type AbstractQuadraticModel{T, S} <: AbstractNLPModel{T, S} end
 
@@ -64,7 +70,10 @@ mutable struct QuadraticModel{T, S, M1, M2} <: AbstractQuadraticModel{T, S}
   data::QPData{T, S, M1, M2}
 end
 
-function Base.convert(::Type{QuadraticModel{T, S, Mconv, Mconv}}, qm::QuadraticModel{T, S, M1, M2}) where {T, S, M1 <: AbstractMatrix, M2 <: AbstractMatrix, Mconv}
+function Base.convert(
+  ::Type{QuadraticModel{T, S, Mconv, Mconv}},
+  qm::QuadraticModel{T, S, M1, M2},
+) where {T, S, M1 <: AbstractMatrix, M2 <: AbstractMatrix, Mconv}
   data_conv = convert(QPData{T, S, Mconv, Mconv}, qm.data)
   return QuadraticModel(qm.meta, qm.counters, data_conv)
 end
@@ -260,18 +269,18 @@ end
 
 function fill_structure!(S::SparseMatrixCSC, rows, cols)
   count = 1
-  @inbounds for col = 1 : size(S, 2), k = S.colptr[col] : (S.colptr[col+1]-1)
-      rows[count] = S.rowval[k]
-      cols[count] = col
-      count += 1
+  @inbounds for col = 1:size(S, 2), k = S.colptr[col]:(S.colptr[col + 1] - 1)
+    rows[count] = S.rowval[k]
+    cols[count] = col
+    count += 1
   end
 end
 
 function fill_coord!(S::SparseMatrixCSC, vals, obj_weight)
   count = 1
-  @inbounds for col = 1 : size(S, 2), k = S.colptr[col] : (S.colptr[col+1]-1)
-      vals[count] = obj_weight * S.nzval[k]
-      count += 1
+  @inbounds for col = 1:size(S, 2), k = S.colptr[col]:(S.colptr[col + 1] - 1)
+    vals[count] = obj_weight * S.nzval[k]
+    count += 1
   end
 end
 
@@ -290,8 +299,8 @@ function NLPModels.hess_structure!(
   cols::AbstractVector{<:Integer},
 ) where {T, S, M1 <: Matrix}
   count = 1
-  for j=1:qp.meta.nvar
-    for i=j:qp.meta.nvar
+  for j = 1:(qp.meta.nvar)
+    for i = j:(qp.meta.nvar)
       rows[count] = i
       cols[count] = j
       count += 1
@@ -330,9 +339,9 @@ function NLPModels.hess_coord!(
 ) where {T, S, M1 <: Matrix}
   NLPModels.increment!(qp, :neval_hess)
   count = 1
-  for j=1:qp.meta.nvar
-    for i=j:qp.meta.nvar
-      vals[count] = obj_weight * qp.data.H[i,j]
+  for j = 1:(qp.meta.nvar)
+    for i = j:(qp.meta.nvar)
+      vals[count] = obj_weight * qp.data.H[i, j]
       count += 1
     end
   end
@@ -372,8 +381,8 @@ function NLPModels.jac_structure!(
   cols::AbstractVector{<:Integer},
 ) where {T, S, M1, M2 <: Matrix}
   count = 1
-  for j=1:qp.meta.nvar
-    for i=1:qp.meta.ncon
+  for j = 1:(qp.meta.nvar)
+    for i = 1:(qp.meta.ncon)
       rows[count] = i
       cols[count] = j
       count += 1
@@ -385,8 +394,8 @@ end
 function NLPModels.jac_coord!(
   qp::QuadraticModel{T, S, M1, M2},
   x::AbstractVector,
-  vals::AbstractVector
-  ) where {T, S, M1, M2 <: SparseMatrixCOO}
+  vals::AbstractVector,
+) where {T, S, M1, M2 <: SparseMatrixCOO}
   NLPModels.increment!(qp, :neval_jac)
   vals .= qp.data.A.vals
   return vals
@@ -395,8 +404,8 @@ end
 function NLPModels.jac_coord!(
   qp::QuadraticModel{T, S, M1, M2},
   x::AbstractVector,
-  vals::AbstractVector
-  ) where {T, S, M1, M2 <: SparseMatrixCSC}
+  vals::AbstractVector,
+) where {T, S, M1, M2 <: SparseMatrixCSC}
   NLPModels.increment!(qp, :neval_jac)
   fill_coord!(qp.data.A, vals, one(T))
   return vals
@@ -405,12 +414,12 @@ end
 function NLPModels.jac_coord!(
   qp::QuadraticModel{T, S, M1, M2},
   x::AbstractVector,
-  vals::AbstractVector
-  ) where {T, S, M1, M2 <: Matrix}
+  vals::AbstractVector,
+) where {T, S, M1, M2 <: Matrix}
   NLPModels.increment!(qp, :neval_jac)
   count = 1
-  for j=1:qp.meta.nvar
-    for i=1:qp.meta.ncon
+  for j = 1:(qp.meta.nvar)
+    for i = 1:(qp.meta.ncon)
       vals[count] = qp.data.A[i, j]
       count += 1
     end
@@ -538,7 +547,10 @@ function slackdata(data::QPData{T}, meta::NLPModelMeta{T}, ns::Int) where {T}
   )
 end
 
-function NLPModelsModifiers.SlackModel(qp::AbstractQuadraticModel{T, S}, name = qp.meta.name * "-slack") where{T, S}
+function NLPModelsModifiers.SlackModel(
+  qp::AbstractQuadraticModel{T, S},
+  name = qp.meta.name * "-slack",
+) where {T, S}
   qp.meta.ncon == length(qp.meta.jfix) && return qp
   nfix = length(qp.meta.jfix)
   ns = qp.meta.ncon - nfix

test/runtests.jl

@@ -3,7 +3,13 @@ using LinearAlgebra, Printf, SparseArrays, Test
 
 # our packages
 using ADNLPModels,
-  LinearOperators, NLPModels, NLPModelsModifiers, NLPModelsTest, QPSReader, QuadraticModels, SparseMatricesCOO
+  LinearOperators,
+  NLPModels,
+  NLPModelsModifiers,
+  NLPModelsTest,
+  QPSReader,
+  QuadraticModels,
+  SparseMatricesCOO
 
 @testset "test utils" begin
   A = rand(10, 10)
@@ -12,7 +18,7 @@ using ADNLPModels,
   @test nnz(Symmetric(A)) == 55
   v1, v2 = rand(10), rand(9)
   @test nnz(SymTridiagonal(v1, v2)) == 19
-  Asp = sparse([1. 0. 0. ; 1. 0. 0. ; 0. 3. 2.])
+  Asp = sparse([1.0 0.0 0.0; 1.0 0.0 0.0; 0.0 3.0 2.0])
   @test nnz(Symmetric(Asp, :L)) == 4
 end
 
@@ -110,14 +116,12 @@ end
     c0 = 0.0,
     name = "QM1",
   )
-  
+
   smdense = SlackModel(qpdense)
   testSM(smdense)
 
-  qpcoo = convert(
-    QuadraticModel{T, Vector{T}, SparseMatrixCOO{T, Int}, SparseMatrixCOO{T, Int}},
-    qpdense
-  )
+  qpcoo =
+    convert(QuadraticModel{T, Vector{T}, SparseMatrixCOO{T, Int}, SparseMatrixCOO{T, Int}}, qpdense)
   @test typeof(qpcoo.data.H) <: SparseMatrixCOO
   @test typeof(qpcoo.data.A) <: SparseMatrixCOO
 end