Use MOI.Nonlinear.QPBlockData

.github/workflows/ci.yml

@@ -41,6 +41,13 @@ jobs:
           version: ${{ matrix.version }}
           arch: ${{ matrix.arch }}
       - uses: julia-actions/cache@v3
+      - name: MOI
+        shell: julia --project=@. {0}
+        run: |
+          using Pkg
+          Pkg.add([
+              PackageSpec(name="MathOptInterface", rev="bl/qp_block_data"),
+          ])
       - uses: julia-actions/julia-buildpkg@v1
       - uses: julia-actions/julia-runtest@v1
         env:

ext/IpoptMathOptInterfaceExt/MOI_wrapper.jl

@@ -3,7 +3,8 @@
 # Use of this source code is governed by an MIT-style license that can be found
 # in the LICENSE.md file or at https://opensource.org/licenses/MIT.
 
-include("utils.jl")
+const QPBlockData = MOI.Nonlinear.QPBlockData
+
 
 const _PARAMETER_OFFSET = 0x00f0000000000000
 
@@ -53,6 +54,10 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
     nlp_dual_start::Union{Nothing,Vector{Float64}}
     mult_g_nlp::Dict{MOI.Nonlinear.ConstraintIndex,Float64}
     qp_data::QPBlockData{Float64}
+    # The number of entries of the Jacobian and of the Hessian of the
+    # Lagrangian of `qp_data`, computed in `_setup_model`.
+    qp_nnzj::Int
+    qp_nnzh::Int
     nlp_model::Union{Nothing,MOI.Nonlinear.Model}
     callback::Union{Nothing,Function}
     barrier_iterations::Int
@@ -84,6 +89,8 @@ mutable struct Optimizer <: MOI.AbstractOptimizer
             nothing,
             Dict{MOI.Nonlinear.ConstraintIndex,Float64}(),
             QPBlockData{Float64}(),
+            0,
+            0,
             nothing,
             nothing,
             0,
@@ -136,6 +143,8 @@ function MOI.empty!(model::Optimizer)
     model.nlp_dual_start = nothing
     empty!(model.mult_g_nlp)
     model.qp_data = QPBlockData{Float64}()
+    model.qp_nnzj = 0
+    model.qp_nnzh = 0
     model.nlp_model = nothing
     model.callback = nothing
     model.barrier_iterations = 0
@@ -193,6 +202,11 @@ function MOI.add_constrained_variable(
     push!(model.list_of_variable_indices, p)
     model.parameters[p] =
         MOI.Nonlinear.add_parameter(model.nlp_model, set.value)
+    # `QPBlockData` treats a variable as a parameter if and only if its index
+    # is a key of `parameters`, so the parameter must be registered before
+    # any structure query. The value is re-synced in `copy_parameters` before
+    # every solve.
+    model.qp_data.parameters[p.value] = set.value
     ci = MOI.ConstraintIndex{MOI.VariableIndex,typeof(set)}(p.value)
     return p, ci
 end
@@ -1181,8 +1195,8 @@ function _eval_constraint_jacobian(
 end
 
 function MOI.eval_constraint_jacobian(model::Optimizer, values, x)
-    offset = MOI.eval_constraint_jacobian(model.qp_data, values, x)
-    offset -= 1  # .qp_data returns one-indexed offset
+    MOI.eval_constraint_jacobian(model.qp_data, values, x)
+    offset = model.qp_nnzj
     for (f, s) in model.vector_nonlinear_oracle_constraints
         offset = _eval_constraint_jacobian(values, offset, x, f, s)
     end
@@ -1234,8 +1248,8 @@ function _eval_hessian_lagrangian(
 end
 
 function MOI.eval_hessian_lagrangian(model::Optimizer, H, x, σ, μ)
-    offset = MOI.eval_hessian_lagrangian(model.qp_data, H, x, σ, μ)
-    offset -= 1  # .qp_data returns one-indexed offset
+    MOI.eval_hessian_lagrangian(model.qp_data, H, x, σ, μ)
+    offset = model.qp_nnzh
     μ_offset = length(model.qp_data)
     for (f, s) in model.vector_nonlinear_oracle_constraints
         offset, μ_offset =
@@ -1376,8 +1390,13 @@ function _setup_model(model::Optimizer)
             MOI.Nonlinear.Evaluator(model.nlp_model, model.ad_backend, vars),
         )
     end
+    model.qp_nnzj = length(MOI.jacobian_structure(model.qp_data))
+    model.qp_nnzh = length(MOI.hessian_lagrangian_structure(model.qp_data))
     has_quadratic_constraints =
-        any(isequal(_kFunctionTypeScalarQuadratic), model.qp_data.function_type)
+        any(
+            isequal(MOI.Nonlinear._kFunctionTypeScalarQuadratic),
+            model.qp_data.function_type,
+        )
     has_nlp_constraints =
         !isempty(model.nlp_data.constraint_bounds) ||
         !isempty(model.vector_nonlinear_oracle_constraints)