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JuliaSmoothOptimizers · Jun 21, 2024 · 3dd5054 · 3dd5054
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Showing 6 changed files with 53 additions and 35 deletions.
diff --git a/src/moi_nlp_model.jl b/src/moi_nlp_model.jl
@@ -29,7 +29,8 @@ end
 
 function nlp_model(moimodel::MOI.ModelLike; hessian::Bool = true, name::String = "Generic")
   index_map, nvar, lvar, uvar, x0 = parser_variables(moimodel)
-  nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon = parser_MOI(moimodel, index_map, nvar)
+  nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon =
+    parser_MOI(moimodel, index_map, nvar)
 
   nlp_data = _nlp_block(moimodel)
   nnln, nlcon, nl_lcon, nl_ucon = parser_NL(nlp_data, hessian = hessian)
@@ -66,7 +67,8 @@ function nlp_model(moimodel::MOI.ModelLike; hessian::Bool = true, name::String =
     name = name,
   )
 
-  return MathOptNLPModel(meta, nlp_data.evaluator, lincon, quadcon, nlcon, λ, obj, Counters()), index_map
+  return MathOptNLPModel(meta, nlp_data.evaluator, lincon, quadcon, nlcon, λ, obj, Counters()),
+  index_map
 end
 
 function NLPModels.obj(nlp::MathOptNLPModel, x::AbstractVector)
@@ -138,8 +140,8 @@ function NLPModels.jac_nln_structure!(
     index = 0
     for i = 1:(nlp.quadcon.nquad)
       qcon = nlp.quadcon.constraints[i]
-      view(rows, index+1:index+qcon.nnzg) .= i
-      view(cols, index+1:index+qcon.nnzg) .= qcon.g
+      view(rows, (index + 1):(index + qcon.nnzg)) .= i
+      view(cols, (index + 1):(index + qcon.nnzg)) .= qcon.g
       index += qcon.nnzg
     end
   end
@@ -161,22 +163,22 @@ function NLPModels.jac_nln_coord!(nlp::MathOptNLPModel, x::AbstractVector, vals:
   increment!(nlp, :neval_jac_nln)
   if nlp.quadcon.nquad > 0
     index = 0
-    view(vals, 1:nlp.quadcon.nnzj) .= 0.0
+    view(vals, 1:(nlp.quadcon.nnzj)) .= 0.0
     for i = 1:(nlp.quadcon.nquad)
       qcon = nlp.quadcon.constraints[i]
       for (j, ind) in enumerate(qcon.b.nzind)
         k = qcon.dg[ind]
-        vals[index+k] += qcon.b.nzval[j]
+        vals[index + k] += qcon.b.nzval[j]
       end
-      for j = 1:qcon.nnzh
+      for j = 1:(qcon.nnzh)
         row = qcon.A.rows[j]
         col = qcon.A.cols[j]
         val = qcon.A.vals[j]
         k1 = qcon.dg[row]
-        vals[index+k1] += val * x[col]
+        vals[index + k1] += val * x[col]
         if row != col
           k2 = qcon.dg[col]
-          vals[index+k2] += val * x[row]
+          vals[index + k2] += val * x[row]
         end
       end
       index += qcon.nnzg
@@ -306,11 +308,12 @@ function NLPModels.hess_coord!(
   end
   if (nlp.obj.type == "NONLINEAR") || (nlp.meta.nnln > nlp.quadcon.nquad)
     λ = view(y, (nlp.meta.nlin + nlp.quadcon.nquad + 1):(nlp.meta.ncon))
-    MOI.eval_hessian_lagrangian(nlp.eval,
+    MOI.eval_hessian_lagrangian(
+      nlp.eval,
       view(vals, (nlp.obj.nnzh + nlp.quadcon.nnzh + 1):(nlp.meta.nnzh)),
       x,
       obj_weight,
-      λ
+      λ,
     )
   end
   if nlp.quadcon.nquad > 0
@@ -364,7 +367,14 @@ function NLPModels.hprod!(
   end
   if nlp.obj.type == "QUADRATIC"
     (nlp.meta.nnln == nlp.quadcon.nquad) && (hv .= 0.0)
-    coo_sym_add_mul!(nlp.obj.hessian.rows, nlp.obj.hessian.cols, nlp.obj.hessian.vals, v, hv, obj_weight)
+    coo_sym_add_mul!(
+      nlp.obj.hessian.rows,
+      nlp.obj.hessian.cols,
+      nlp.obj.hessian.vals,
+      v,
+      hv,
+      obj_weight,
+    )
   end
   if nlp.quadcon.nquad > 0
     (nlp.obj.type == "LINEAR") && (hv .= 0.0)
@@ -389,7 +399,14 @@ function NLPModels.hprod!(
   end
   if nlp.obj.type == "QUADRATIC"
     hv .= 0.0
-    coo_sym_add_mul!(nlp.obj.hessian.rows, nlp.obj.hessian.cols, nlp.obj.hessian.vals, v, hv, obj_weight)
+    coo_sym_add_mul!(
+      nlp.obj.hessian.rows,
+      nlp.obj.hessian.cols,
+      nlp.obj.hessian.vals,
+      v,
+      hv,
+      obj_weight,
+    )
   end
   if nlp.obj.type == "NONLINEAR"
     MOI.eval_hessian_lagrangian_product(nlp.eval, hv, x, v, obj_weight, nlp.λ)

diff --git a/src/moi_nls_model.jl b/src/moi_nls_model.jl
@@ -32,7 +32,8 @@ function MathOptNLSModel(cmodel::JuMP.Model, F; hessian::Bool = true, name::Stri
 
   _nlp_sync!(cmodel)
   moimodel = backend(cmodel)
-  nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon = parser_MOI(moimodel, index_map, nvar)
+  nlin, lincon, lin_lcon, lin_ucon, quadcon, quad_lcon, quad_ucon =
+    parser_MOI(moimodel, index_map, nvar)
 
   nlp_data = _nlp_block(moimodel)
   nnln, nlcon, nl_lcon, nl_ucon = parser_NL(nlp_data, hessian = hessian)
@@ -287,8 +288,8 @@ function NLPModels.jac_nln_structure!(
     index = 0
     for i = 1:(nls.quadcon.nquad)
       qcon = nls.quadcon.constraints[i]
-      view(rows, index+1:index+qcon.nnzg) .= i
-      view(cols, index+1:index+qcon.nnzg) .= qcon.g
+      view(rows, (index + 1):(index + qcon.nnzg)) .= i
+      view(cols, (index + 1):(index + qcon.nnzg)) .= qcon.g
       index += qcon.nnzg
     end
   end
@@ -310,22 +311,22 @@ function NLPModels.jac_nln_coord!(nls::MathOptNLSModel, x::AbstractVector, vals:
   increment!(nls, :neval_jac_nln)
   if nls.quadcon.nquad > 0
     index = 0
-    view(vals, 1:nls.quadcon.nnzj) .= 0.0
+    view(vals, 1:(nls.quadcon.nnzj)) .= 0.0
     for i = 1:(nls.quadcon.nquad)
       qcon = nls.quadcon.constraints[i]
       for (j, ind) in enumerate(qcon.b.nzind)
         k = qcon.dg[ind]
-        vals[index+k] += qcon.b.nzval[j]
+        vals[index + k] += qcon.b.nzval[j]
       end
-      for j = 1:qcon.nnzh
+      for j = 1:(qcon.nnzh)
         row = qcon.A.rows[j]
         col = qcon.A.cols[j]
         val = qcon.A.vals[j]
         k1 = qcon.dg[row]
-        vals[index+k1] += val * x[col]
+        vals[index + k1] += val * x[col]
         if row != col
           k2 = qcon.dg[col]
-          vals[index+k2] += val * x[row]
+          vals[index + k2] += val * x[row]
         end
       end
       index += qcon.nnzg
@@ -460,7 +461,7 @@ function NLPModels.hess_coord!(
       view(vals, (nls.lls.nnzh + nls.quadcon.nnzh + 1):(nls.meta.nnzh)),
       x,
       obj_weight,
-      λ
+      λ,
     )
   end
   if nls.quadcon.nquad > 0

diff --git a/src/utils.jl b/src/utils.jl
@@ -12,12 +12,12 @@ const VI = MOI.VariableIndex  # VariableIndex(value)
 # ScalarAffineFunctions and VectorAffineFunctions
 const SAF = MOI.ScalarAffineFunction{Float64}  # ScalarAffineFunction{T}(terms, constant)
 const VAF = MOI.VectorAffineFunction{Float64}  # VectorAffineFunction{T}(terms, constants)
-const AF  = Union{SAF, VAF}
+const AF = Union{SAF, VAF}
 
 # ScalarQuadraticFunctions and VectorQuadraticFunctions
 const SQF = MOI.ScalarQuadraticFunction{Float64}  # ScalarQuadraticFunction{T}(affine_terms, quadratic_terms, constant)
 const VQF = MOI.VectorQuadraticFunction{Float64}  # VectorQuadraticFunction{T}(affine_terms, quadratic_terms, constants)
-const QF  = Union{SQF, VQF}
+const QF = Union{SQF, VQF}
 
 # AffLinSets and VecLinSets
 const ALS = Union{
@@ -57,7 +57,7 @@ mutable struct QuadraticConstraint
   A::COO
   b::SparseVector{Float64}
   g::Vector{Int}
-  dg::Dict{Int,Int}
+  dg::Dict{Int, Int}
   nnzg::Int
   nnzh::Int
 end
@@ -279,7 +279,7 @@ function parser_SQF(fun, set, nvar, qcons, quad_lcon, quad_ucon, index_map)
   # - The key `r` specifies a row index in the vector Ax + b.
   # - The value `dg[r]` is a position in the vector (of length nnzg)
   # where the non-zero entries of the Jacobian for row `r` are stored.
-  dg = Dict{Int,Int}(g[p] => p for p = 1:nnzg)
+  dg = Dict{Int, Int}(g[p] => p for p = 1:nnzg)
   nnzh = length(vals)
   qcon = QuadraticConstraint(A, b, g, dg, nnzg, nnzh)
   push!(qcons, qcon)
@@ -345,7 +345,7 @@ function parser_VQF(fun, set, nvar, qcons, quad_lcon, quad_ucon, index_map)
     # - The key `r` specifies a row index in the vector Ax + b.
     # - The value `dg[r]` is a position in the vector (of length nnzg)
     # where the non-zero entries of the Jacobian for row `r` are stored.
-    dg = Dict{Int,Int}(g[p] => p for p = 1:nnzg)
+    dg = Dict{Int, Int}(g[p] => p for p = 1:nnzg)
     nnzh = length(vals)
     qcon = QuadraticConstraint(A, b, g, dg, nnzg, nnzh)
     push!(qcons, qcon)
@@ -375,7 +375,11 @@ function parser_MOI(moimodel, index_map, nvar)
 
   contypes = MOI.get(moimodel, MOI.ListOfConstraintTypesPresent())
   for (F, S) in contypes
-    F <: AF || F <: QF || F == MOI.ScalarNonlinearFunction || F == VI || error("Function $F is not supported.")
+    F <: AF ||
+      F <: QF ||
+      F == MOI.ScalarNonlinearFunction ||
+      F == VI ||
+      error("Function $F is not supported.")
     S <: LS || error("Set $S is not supported.")
 
     conindices = MOI.get(moimodel, MOI.ListOfConstraintIndices{F, S}())

diff --git a/test/nlp_consistency.jl b/test/nlp_consistency.jl
@@ -4,7 +4,7 @@ for problem in nlp_problems
     problem_f = eval(Symbol(lowercase(problem)))
     nlp_moi = MathOptNLPModel(problem_f())
     nlps = [nlp_manual; nlp_moi]
-    consistent_nlps(nlps, linear_api=true, test_slack=false)
+    consistent_nlps(nlps, linear_api = true, test_slack = false)
     view_subarray_nlp(nlp_moi)
   end
 end
diff --git a/test/nlp_problems/hs219.jl b/test/nlp_problems/hs219.jl
@@ -6,11 +6,7 @@ function hs219(args...; kwargs...)
   @constraint(nlp, x[1]^2 - x[2] - x[4]^2 == 0)
   @NLconstraint(nlp, x[2] - x[1]^3 - x[3]^2 == 0)
 
-  @NLobjective(
-    nlp,
-    Min,
-    -x[1]
-  )
+  @NLobjective(nlp, Min, -x[1])
 
   return nlp
 end
diff --git a/test/nls_consistency.jl b/test/nls_consistency.jl
@@ -8,7 +8,7 @@ for problem in nls_problems
     if isdefined(Main, Symbol(spc))
       push!(nlss, eval(Meta.parse(spc))())
     end
-    consistent_nlss(nlss, linear_api=true, test_slack=false)
+    consistent_nlss(nlss, linear_api = true, test_slack = false)
     view_subarray_nls(nls_moi)
   end
 end