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Support quadratic constraints #181

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merged 21 commits into from
Jun 21, 2024
Merged

Support quadratic constraints #181

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07d8e1f
Support quadratic constraints
amontoison Jun 20, 2024
4dc88ce
Update utils.jl
amontoison Jun 20, 2024
2be947c
Fix a typo
amontoison Jun 20, 2024
dd738ba
Add a function parser_VQF
amontoison Jun 20, 2024
d80355f
test_moi_nlp_model.jl
amontoison Jun 20, 2024
9f9ba02
Fix two mistakes
amontoison Jun 20, 2024
ebee588
Fix another bug
amontoison Jun 20, 2024
7d9f5a1
Fix MathOptNLPModel
amontoison Jun 20, 2024
b715431
Support quadratic constraints in MathOptNLSModel
amontoison Jun 20, 2024
0810e40
Replace nlp by nls in MathOptNLSModel
amontoison Jun 20, 2024
547401a
Return an error if we encounter an unsupported feature
amontoison Jun 20, 2024
9952897
Add Lagrange multipliers for hess_coord! and hprod! without y in Math…
amontoison Jun 20, 2024
0b227b4
Fix the size of lambda in MathOptNLPModel and MathOptNLSModel
amontoison Jun 20, 2024
9b33bee
Fix many bugs...
amontoison Jun 21, 2024
8d10910
nlp -> nls
amontoison Jun 21, 2024
c8713cb
Update problems to test quadratic constraints
amontoison Jun 21, 2024
3eb3a5f
Update a constraint in hs100.jl
amontoison Jun 21, 2024
6b4c1fb
Update nlp_consistency.jl
amontoison Jun 21, 2024
02317b4
Update nls_consistency.jl
amontoison Jun 21, 2024
7efb9e2
Fix errors in parser_VQF
amontoison Jun 21, 2024
f39f1e4
Improve utils.jl
amontoison Jun 21, 2024
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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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