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Support quadratic constraints (#181)
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@amontoison
amontoison authored Jun 21, 2024
1 parent 5bb0b82 commit c12dc0c
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Showing 17 changed files with 540 additions and 100 deletions.
162 changes: 126 additions & 36 deletions src/moi_nlp_model.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,9 @@ mutable struct MathOptNLPModel <: AbstractNLPModel{Float64, Vector{Float64}}
meta::NLPModelMeta{Float64, Vector{Float64}}
eval::MOI.Nonlinear.Evaluator
lincon::LinearConstraints
quadcon::QuadraticConstraints
nlcon::NonLinearStructure
λ::Vector{Float64}
obj::Objective
counters::Counters
end
Expand All @@ -27,22 +29,23 @@ 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 = parser_MOI(moimodel, index_map)
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)
λ = zeros(nnln) # Lagrange multipliers for hess_coord! and hprod! without y

if nlp_data.has_objective
obj = Objective("NONLINEAR", 0.0, spzeros(Float64, nvar), COO(), 0)
else
obj = parser_objective_MOI(moimodel, nvar, index_map)
end

ncon = nlin + nnln
lcon = vcat(lin_lcon, nl_lcon)
ucon = vcat(lin_ucon, nl_ucon)
nnzj = lincon.nnzj + nlcon.nnzj
nnzh = obj.nnzh + nlcon.nnzh
ncon = nlin + quadcon.nquad + nnln
lcon = vcat(lin_lcon, quad_lcon, nl_lcon)
ucon = vcat(lin_ucon, quad_ucon, nl_ucon)
nnzj = lincon.nnzj + quadcon.nnzj + nlcon.nnzj
nnzh = obj.nnzh + quadcon.nnzh + nlcon.nnzh

meta = NLPModelMeta(
nvar,
Expand All @@ -57,13 +60,13 @@ function nlp_model(moimodel::MOI.ModelLike; hessian::Bool = true, name::String =
nnzh = nnzh,
lin = collect(1:nlin),
lin_nnzj = lincon.nnzj,
nln_nnzj = nlcon.nnzj,
nln_nnzj = quadcon.nnzj + nlcon.nnzj,
minimize = MOI.get(moimodel, MOI.ObjectiveSense()) == MOI.MIN_SENSE,
islp = (obj.type == "LINEAR") && (nnln == 0),
islp = (obj.type == "LINEAR") && (nnln == 0) && (quadcon.nquad == 0),
name = name,
)

return MathOptNLPModel(meta, nlp_data.evaluator, lincon, nlcon, 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)
Expand Down Expand Up @@ -106,7 +109,13 @@ end

function NLPModels.cons_nln!(nlp::MathOptNLPModel, x::AbstractVector, c::AbstractVector)
increment!(nlp, :neval_cons_nln)
MOI.eval_constraint(nlp.eval, c, x)
for i = 1:(nlp.quadcon.nquad)
qcon = nlp.quadcon.constraints[i]
c[i] = 0.5 * coo_sym_dot(qcon.A.rows, qcon.A.cols, qcon.A.vals, x, x) + dot(qcon.b, x)
end
if nlp.meta.nnln > nlp.quadcon.nquad
MOI.eval_constraint(nlp.eval, view(c, (nlp.quadcon.nquad + 1):(nlp.meta.nnln)), x)
end
return c
end

Expand All @@ -125,8 +134,20 @@ function NLPModels.jac_nln_structure!(
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
)
view(rows, 1:(nlp.nlcon.nnzj)) .= nlp.nlcon.jac_rows
view(cols, 1:(nlp.nlcon.nnzj)) .= nlp.nlcon.jac_cols
if nlp.quadcon.nquad > 0
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
index += qcon.nnzg
end
end
if nlp.meta.nnln > nlp.quadcon.nquad
ind_nnln = (nlp.quadcon.nnzj + 1):(nlp.quadcon.nnzj + nlp.nlcon.nnzj)
view(rows, ind_nnln) .= nlp.quadcon.nquad .+ nlp.nlcon.jac_rows
view(cols, ind_nnln) .= nlp.nlcon.jac_cols
end
return rows, cols
end

Expand All @@ -138,7 +159,33 @@ end

function NLPModels.jac_nln_coord!(nlp::MathOptNLPModel, x::AbstractVector, vals::AbstractVector)
increment!(nlp, :neval_jac_nln)
MOI.eval_constraint_jacobian(nlp.eval, view(vals, 1:(nlp.nlcon.nnzj)), x)
if nlp.quadcon.nquad > 0
index = 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]
end
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]
if row != col
k2 = qcon.dg[col]
vals[index+k2] += val * x[row]
end
end
index += qcon.nnzg
end
end
if nlp.meta.nnln > nlp.quadcon.nquad
ind_nnln = (nlp.quadcon.nnzj + 1):(nlp.quadcon.nnzj + nlp.nlcon.nnzj)
MOI.eval_constraint_jacobian(nlp.eval, view(vals, ind_nnln), x)
end
return vals
end

Expand Down Expand Up @@ -166,7 +213,18 @@ function NLPModels.jprod_nln!(
Jv::AbstractVector,
)
increment!(nlp, :neval_jprod_nln)
MOI.eval_constraint_jacobian_product(nlp.eval, Jv, x, v)
if nlp.meta.nnln > nlp.quadcon.nquad
ind_nnln = (nlp.quadcon.nquad + 1):(nlp.meta.nnln)
MOI.eval_constraint_jacobian_product(nlp.eval, view(Jv, ind_nnln), x, v)
end
(nlp.meta.nnln == nlp.quadcon.nquad) && (Jv .= 0.0)
if nlp.quadcon.nquad > 0
for i = 1:(nlp.quadcon.nquad)
# Jv[i] = (Aᵢ * x + bᵢ)ᵀ * v
qcon = nlp.quadcon.constraints[i]
Jv[i] += coo_sym_dot(qcon.A.rows, qcon.A.cols, qcon.A.vals, x, v) + dot(qcon.b, v)
end
end
return Jv
end

Expand Down Expand Up @@ -194,7 +252,19 @@ function NLPModels.jtprod_nln!(
Jtv::AbstractVector,
)
increment!(nlp, :neval_jtprod_nln)
MOI.eval_constraint_jacobian_transpose_product(nlp.eval, Jtv, x, v)
if nlp.meta.nnln > nlp.quadcon.nquad
ind_nnln = (nlp.quadcon.nquad + 1):(nlp.meta.nnln)
MOI.eval_constraint_jacobian_transpose_product(nlp.eval, Jtv, x, view(v, ind_nnln))
end
(nlp.meta.nnln == nlp.quadcon.nquad) && (Jtv .= 0.0)
if nlp.quadcon.nquad > 0
for i = 1:(nlp.quadcon.nquad)
# Jtv += v[i] * (Aᵢ * x + bᵢ)
qcon = nlp.quadcon.constraints[i]
coo_sym_add_mul!(qcon.A.rows, qcon.A.cols, qcon.A.vals, x, Jtv, v[i])
Jtv .+= v[i] .* qcon.b
end
end
return Jtv
end

Expand All @@ -207,9 +277,18 @@ function NLPModels.hess_structure!(
view(rows, 1:(nlp.obj.nnzh)) .= nlp.obj.hessian.rows
view(cols, 1:(nlp.obj.nnzh)) .= nlp.obj.hessian.cols
end
if (nlp.obj.type == "NONLINEAR") || (nlp.meta.nnln > 0)
view(rows, (nlp.obj.nnzh + 1):(nlp.meta.nnzh)) .= nlp.nlcon.hess_rows
view(cols, (nlp.obj.nnzh + 1):(nlp.meta.nnzh)) .= nlp.nlcon.hess_cols
if (nlp.obj.type == "NONLINEAR") || (nlp.meta.nnln > nlp.quadcon.nquad)
view(rows, (nlp.obj.nnzh + nlp.quadcon.nnzh + 1):(nlp.meta.nnzh)) .= nlp.nlcon.hess_rows
view(cols, (nlp.obj.nnzh + nlp.quadcon.nnzh + 1):(nlp.meta.nnzh)) .= nlp.nlcon.hess_cols
end
if nlp.quadcon.nquad > 0
index = nlp.obj.nnzh
for i = 1:(nlp.quadcon.nquad)
qcon = nlp.quadcon.constraints[i]
view(rows, (index + 1):(index + qcon.nnzh)) .= qcon.A.rows
view(cols, (index + 1):(index + qcon.nnzh)) .= qcon.A.cols
index += qcon.nnzh
end
end
return rows, cols
end
Expand All @@ -225,15 +304,23 @@ function NLPModels.hess_coord!(
if nlp.obj.type == "QUADRATIC"
view(vals, 1:(nlp.obj.nnzh)) .= obj_weight .* nlp.obj.hessian.vals
end
if (nlp.obj.type == "NONLINEAR") || (nlp.meta.nnln > 0)
MOI.eval_hessian_lagrangian(
nlp.eval,
view(vals, (nlp.obj.nnzh + 1):(nlp.meta.nnzh)),
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,
view(vals, (nlp.obj.nnzh + nlp.quadcon.nnzh + 1):(nlp.meta.nnzh)),
x,
obj_weight,
view(y, nlp.meta.nln),
λ
)
end
if nlp.quadcon.nquad > 0
index = nlp.obj.nnzh
for i = 1:(nlp.quadcon.nquad)
qcon = nlp.quadcon.constraints[i]
view(vals, (index + 1):(index + qcon.nnzh)) .= y[nlp.meta.nlin + i] .* qcon.A.vals
index += qcon.nnzh
end
end
return vals
end

Expand All @@ -252,7 +339,9 @@ function NLPModels.hess_coord!(
view(vals, (nlp.obj.nnzh + 1):(nlp.meta.nnzh)) .= 0.0
end
if nlp.obj.type == "NONLINEAR"
MOI.eval_hessian_lagrangian(nlp.eval, vals, x, obj_weight, zeros(nlp.meta.nnln))
view(vals, 1:(nlp.obj.nnzh + nlp.quadcon.nnzh)) .= 0.0
ind_nnln = (nlp.obj.nnzh + nlp.quadcon.nnzh + 1):(nlp.meta.nnzh)
MOI.eval_hessian_lagrangian(nlp.eval, view(vals, ind_nnln), x, obj_weight, nlp.λ)
end
return vals
end
Expand All @@ -269,19 +358,20 @@ function NLPModels.hprod!(
if (nlp.obj.type == "LINEAR") && (nlp.meta.nnln == 0)
hv .= 0.0
end
if (nlp.obj.type == "NONLINEAR") || (nlp.meta.nnln > 0)
MOI.eval_hessian_lagrangian_product(nlp.eval, hv, x, v, obj_weight, view(y, nlp.meta.nln))
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_product(nlp.eval, hv, x, v, obj_weight, λ)
end
if nlp.obj.type == "QUADRATIC"
nlp.meta.nnln == 0 && (hv .= 0.0)
coo_sym_add_mul!(
nlp.obj.hessian.rows,
nlp.obj.hessian.cols,
nlp.obj.hessian.vals,
v,
hv,
obj_weight,
)
(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)
end
if nlp.quadcon.nquad > 0
(nlp.obj.type == "LINEAR") && (hv .= 0.0)
for i = 1:(nlp.quadcon.nquad)
qcon = nlp.quadcon.constraints[i]
coo_sym_add_mul!(qcon.A.rows, qcon.A.cols, qcon.A.vals, v, hv, y[nlp.meta.nlin + i])
end
end
return hv
end
Expand All @@ -302,7 +392,7 @@ function NLPModels.hprod!(
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, zeros(nlp.meta.nnln))
MOI.eval_hessian_lagrangian_product(nlp.eval, hv, x, v, obj_weight, nlp.λ)
end
return hv
end
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