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Merge pull request #107 from JuliaSmoothOptimizers/add-linear-api
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Add linear API
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@tmigot
tmigot authored May 6, 2022
2 parents e2963f0 + 69609ad commit c05a30f
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Showing 2 changed files with 248 additions and 82 deletions.
165 changes: 124 additions & 41 deletions src/moi_nlp_model.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -59,6 +59,8 @@ function MathOptNLPModel(jmodel::JuMP.Model; hessian::Bool = true, name::String
nnzj = nnzj,
nnzh = nnzh,
lin = collect(1:nlin),
lin_nnzj = lincon.nnzj,
nln_nnzj = nl_nnzj,
minimize = objective_sense(jmodel) == MOI.MIN_SENSE,
islp = (obj.type == "LINEAR") && (nnln == 0),
name = name,
Expand Down Expand Up @@ -99,76 +101,107 @@ function NLPModels.grad!(nlp::MathOptNLPModel, x::AbstractVector, g::AbstractVec
return g
end

function NLPModels.cons!(nlp::MathOptNLPModel, x::AbstractVector, c::AbstractVector)
increment!(nlp, :neval_cons)
if nlp.meta.nlin > 0
coo_prod!(
nlp.lincon.jacobian.rows,
nlp.lincon.jacobian.cols,
nlp.lincon.jacobian.vals,
x,
view(c, nlp.meta.lin),
)
end
if nlp.meta.nnln > 0
MOI.eval_constraint(nlp.eval, view(c, nlp.meta.nln), x)
end
function NLPModels.cons_lin!(nlp::MathOptNLPModel, x::AbstractVector, c::AbstractVector)
increment!(nlp, :neval_cons_lin)
coo_prod!(
nlp.lincon.jacobian.rows,
nlp.lincon.jacobian.cols,
nlp.lincon.jacobian.vals,
x,
c,
)
return c
end

function NLPModels.jac_structure!(
function NLPModels.cons_nln!(nlp::MathOptNLPModel, x::AbstractVector, c::AbstractVector)
increment!(nlp, :neval_cons_nln)
MOI.eval_constraint(nlp.eval, c, x)
return c
end

function NLPModels.jac_lin_structure!(
nlp::MathOptNLPModel,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
)
if nlp.meta.nlin > 0
rows[1:(nlp.lincon.nnzj)] .= nlp.lincon.jacobian.rows[1:(nlp.lincon.nnzj)]
cols[1:(nlp.lincon.nnzj)] .= nlp.lincon.jacobian.cols[1:(nlp.lincon.nnzj)]
end
if nlp.meta.nnln > 0
jac_struct = MOI.jacobian_structure(nlp.eval)
for index = (nlp.lincon.nnzj + 1):(nlp.meta.nnzj)
row, col = jac_struct[index - nlp.lincon.nnzj]
rows[index] = nlp.meta.nlin + row
cols[index] = col
end
end
rows[1:(nlp.lincon.nnzj)] .= nlp.lincon.jacobian.rows[1:(nlp.lincon.nnzj)]
cols[1:(nlp.lincon.nnzj)] .= nlp.lincon.jacobian.cols[1:(nlp.lincon.nnzj)]
return rows, cols
end

function NLPModels.jac_coord!(nlp::MathOptNLPModel, x::AbstractVector, vals::AbstractVector)
increment!(nlp, :neval_jac)
if nlp.meta.nlin > 0
vals[1:(nlp.lincon.nnzj)] .= nlp.lincon.jacobian.vals[1:(nlp.lincon.nnzj)]
end
if nlp.meta.nnln > 0
MOI.eval_constraint_jacobian(nlp.eval, view(vals, (nlp.lincon.nnzj + 1):(nlp.meta.nnzj)), x)
function NLPModels.jac_nln_structure!(
nlp::MathOptNLPModel,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
)
jac_struct = MOI.jacobian_structure(nlp.eval)
for index = 1:(nlp.meta.nln_nnzj)
row, col = jac_struct[index]
rows[index] = row
cols[index] = col
end
return rows, cols
end

function NLPModels.jac_lin_coord!(nlp::MathOptNLPModel, x::AbstractVector, vals::AbstractVector)
increment!(nlp, :neval_jac_lin)
vals[1:(nlp.lincon.nnzj)] .= nlp.lincon.jacobian.vals[1:(nlp.lincon.nnzj)]
return vals
end

function NLPModels.jprod!(
function NLPModels.jac_nln_coord!(nlp::MathOptNLPModel, x::AbstractVector, vals::AbstractVector)
increment!(nlp, :neval_jac_nln)
MOI.eval_constraint_jacobian(nlp.eval, vals, x)
return vals
end

function NLPModels.jprod_lin!(
nlp::MathOptNLPModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
v::AbstractVector,
Jv::AbstractVector,
)
vals = jac_coord(nlp, x)
decrement!(nlp, :neval_jac)
jprod!(nlp, rows, cols, vals, v, Jv)
vals = jac_lin_coord(nlp, x)
decrement!(nlp, :neval_jac_lin)
jprod_lin!(nlp, rows, cols, vals, v, Jv)
return Jv
end

function NLPModels.jprod_nln!(
nlp::MathOptNLPModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
v::AbstractVector,
Jv::AbstractVector,
)
vals = jac_nln_coord(nlp, x)
decrement!(nlp, :neval_jac_nln)
jprod_nln!(nlp, rows, cols, vals, v, Jv)
return Jv
end

function NLPModels.jprod_lin!(
nlp::MathOptNLPModel,
x::AbstractVector,
v::AbstractVector,
Jv::AbstractVector,
)
rows, cols = jac_lin_structure(nlp)
jprod_lin!(nlp, x, rows, cols, v, Jv)
return Jv
end

function NLPModels.jprod!(
function NLPModels.jprod_nln!(
nlp::MathOptNLPModel,
x::AbstractVector,
v::AbstractVector,
Jv::AbstractVector,
)
rows, cols = jac_structure(nlp)
jprod!(nlp, x, rows, cols, v, Jv)
rows, cols = jac_nln_structure(nlp)
jprod_nln!(nlp, x, rows, cols, v, Jv)
return Jv
end

Expand Down Expand Up @@ -197,6 +230,56 @@ function NLPModels.jtprod!(
return Jtv
end

function NLPModels.jtprod_lin!(
nlp::MathOptNLPModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
v::AbstractVector,
Jtv::AbstractVector,
)
vals = jac_lin_coord(nlp, x)
decrement!(nlp, :neval_jac_lin)
jtprod_lin!(nlp, rows, cols, vals, v, Jtv)
return Jtv
end

function NLPModels.jtprod_lin!(
nlp::MathOptNLPModel,
x::AbstractVector,
v::AbstractVector,
Jtv::AbstractVector,
)
(rows, cols) = jac_lin_structure(nlp)
jtprod_lin!(nlp, x, rows, cols, v, Jtv)
return Jtv
end

function NLPModels.jtprod_nln!(
nlp::MathOptNLPModel,
x::AbstractVector,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
v::AbstractVector,
Jtv::AbstractVector,
)
vals = jac_nln_coord(nlp, x)
decrement!(nlp, :neval_jac_nln)
jtprod_nln!(nlp, rows, cols, vals, v, Jtv)
return Jtv
end

function NLPModels.jtprod_nln!(
nlp::MathOptNLPModel,
x::AbstractVector,
v::AbstractVector,
Jtv::AbstractVector,
)
(rows, cols) = jac_nln_structure(nlp)
jtprod_nln!(nlp, x, rows, cols, v, Jtv)
return Jtv
end

# Uncomment when :JacVec becomes available in MOI.
#
# function NLPModels.jprod!(nlp :: MathOptNLPModel, x :: AbstractVector, v :: AbstractVector, Jv :: AbstractVector)
Expand Down
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