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Add linear API #140

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206 changes: 202 additions & 4 deletions src/ampl_model.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -322,6 +322,36 @@ end

NLPModels.jth_con(nlp::AmplModel, x::AbstractVector, j::Int) = jth_con(nlp, Vector{Cdouble}(x), j)

function NLPModels.cons_lin!(nlp::AmplModel, x::AbstractVector, c::AbstractVector)
@check_ampl_model
length(c) >= nlp.meta.nlin || error("x must have length at least $(nlp.meta.nlin)")
length(x) >= nlp.meta.nvar || error("x must have length at least $(nlp.meta.nvar)")

k = 1
for j in nlp.meta.lin
c[k] = jth_con(nlp, x, j)
k += 1
nlp.counters.neval_jcon -= 1
end
nlp.counters.neval_cons_lin += 1
return c
end

function NLPModels.cons_nln!(nlp::AmplModel, x::AbstractVector, c::AbstractVector)
@check_ampl_model
length(c) >= nlp.meta.nnln || error("x must have length at least $(nlp.meta.nnln)")
length(x) >= nlp.meta.nvar || error("x must have length at least $(nlp.meta.nvar)")

k = 1
for j in nlp.meta.nln
c[k] = jth_con(nlp, x, j)
k += 1
nlp.counters.neval_jcon -= 1
end
nlp.counters.neval_cons_nln += 1
return c
end

function NLPModels.jth_congrad!(nlp::AmplModel, x::Vector{Cdouble}, j::Int, g::Vector{Cdouble})
@check_ampl_model
(1 <= j <= nlp.meta.ncon) || error("expected 0 ≤ j ≤ $(nlp.meta.ncon)")
Expand Down Expand Up @@ -409,6 +439,143 @@ function NLPModels.jac_structure!(
return rows, cols
end

#=
function _jac_structure(nlp)
rows = Vector{Cint}(undef, nlp.meta.nnzj)
cols = Vector{Cint}(undef, nlp.meta.nnzj)
@asl_call(
:asl_jac_structure,
Nothing,
(Ptr{Nothing}, Ptr{Int32}, Ptr{Int32}),
nlp.__asl,
rows,
cols
)
# Use 1-based indexing.
@. rows[1:(nlp.meta.nnzj)] += Cint(1)
@. cols[1:(nlp.meta.nnzj)] += Cint(1)
return rows, cols
end
=#

###########################################################################################

function NLPModels.jac_lin_structure!(
nlp::AmplModel,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
)
# rows_ = Vector{Cint}(undef, nlp.meta.lin_nnzj)
# cols_ = Vector{Cint}(undef, nlp.meta.lin_nnzj)

#=
for j in nlp.meta.lin
nnzj = Cint(@asl_call(:asl_sparse_congrad_nnz, Csize_t, (Ptr{Nothing}, Cint), nlp.__asl, j - 1))
# How to get the structure separately?
# jth_congrad!(nlp, x, j, g::AbstractVector)
end
=#

rows_, cols_ = jac_structure(nlp)
k = 1
for i =1:nlp.meta.nnzj
if rows_[i] in nlp.meta.lin
rows[k] = findfirst(x -> x == rows_[i], nlp.meta.lin) # __rows[i] - nlp.meta.nnln
cols[k] = cols_[i]
k+=1
end
end

return rows, cols
end

function NLPModels.jac_lin_coord!(
nlp::AmplModel,
x::AbstractVector,
vals::AbstractVector{<:AbstractFloat},
)
#=
vals_ = Vector{Cdouble}(undef, nlp.meta.lin_nnzj)

for j in nlp.meta.lin
nnzj = @asl_call(:asl_sparse_congrad_nnz, Csize_t, (Ptr{Nothing}, Cint), asl, j - 1)
# How to get the structure separately?
# jth_congrad!(nlp, x, j, g::AbstractVector)
end
=#

rows_, cols_ = jac_structure(nlp)
__vals = jac_coord(nlp, x)
nlp.counters.neval_jac -= 1
k = 1
for i =1:nlp.meta.nnzj
if rows_[i] in nlp.meta.lin
vals[k] = __vals[i]
k+=1
end
end
nlp.counters.neval_jac_lin += 1

return vals
end

function NLPModels.jac_nln_structure!(
nlp::AmplModel,
rows::AbstractVector{<:Integer},
cols::AbstractVector{<:Integer},
)
#=
for j in nlp.meta.nln
nnzj = @asl_call(:asl_sparse_congrad_nnz, Csize_t, (Ptr{Nothing}, Cint), nlp.__asl, j - 1)
# How to get the structure separately?
# jth_congrad!(nlp, x, j, g::AbstractVector)
end
=#

rows_, cols_ = jac_structure(nlp)
k = 1
for i =1:nlp.meta.nnzj
if rows_[i] in nlp.meta.nln
rows[k] = findfirst(x -> x == rows_[i], nlp.meta.nln) # __rows[i]
cols[k] = cols_[i]
k+=1
end
end

return rows, cols
end

function NLPModels.jac_nln_coord!(
nlp::AmplModel,
x::AbstractVector,
vals::AbstractVector{<:AbstractFloat},
)
#=
vals_ = Vector{Cdouble}(undef, nlp.meta.nln_nnzj)

for j in nlp.meta.nln
nnzj = @asl_call(:asl_sparse_congrad_nnz, Csize_t, (Ptr{Nothing}, Cint), asl, j - 1)
# How to get the structure separately?
# jth_congrad!(nlp, x, j, g::AbstractVector)
end
=#
rows_, cols_ = jac_structure(nlp)
__vals = jac_coord(nlp, x)
nlp.counters.neval_jac -= 1
k = 1
for i =1:nlp.meta.nnzj
if rows_[i] in nlp.meta.nln
vals[k] = __vals[i]
k+=1
end
end
nlp.counters.neval_jac_nln += 1

return vals
end

###########################################################################################

function NLPModels.jac_coord!(nlp::AmplModel, x::Vector{Cdouble}, vals::Vector{Cdouble})
@check_ampl_model
length(x) >= nlp.meta.nvar || error("x must have length at least $(nlp.meta.nvar)")
Expand Down Expand Up @@ -442,10 +609,17 @@ function NLPModels.jac_coord!(
return vals
end

function NLPModels.jprod!(nlp::AmplModel, x::AbstractVector, v::AbstractVector, Jv::AbstractVector)
nlp.counters.neval_jac -= 1
nlp.counters.neval_jprod += 1
Jv[1:(nlp.meta.ncon)] = jac(nlp, Vector{Cdouble}(x)) * v
function NLPModels.jprod_lin!(nlp::AmplModel, x::AbstractVector, v::AbstractVector, Jv::AbstractVector)
nlp.counters.neval_jac_lin -= 1
nlp.counters.neval_jprod_lin += 1
Jv[1:(nlp.meta.nlin)] = jac_lin(nlp, Vector{Cdouble}(x)) * v
return Jv
end

function NLPModels.jprod_nln!(nlp::AmplModel, x::AbstractVector, v::AbstractVector, Jv::AbstractVector)
nlp.counters.neval_jac_nln -= 1
nlp.counters.neval_jprod_nln += 1
Jv[1:(nlp.meta.nnln)] = jac_nln(nlp, Vector{Cdouble}(x)) * v
return Jv
end

Expand All @@ -461,6 +635,30 @@ function NLPModels.jtprod!(
return Jtv
end

function NLPModels.jtprod_lin!(
nlp::AmplModel,
x::AbstractVector,
v::AbstractVector,
Jtv::AbstractVector,
)
nlp.counters.neval_jac_lin -= 1
nlp.counters.neval_jtprod_lin += 1
Jtv[1:(nlp.meta.nvar)] = jac_lin(nlp, Vector{Cdouble}(x))' * v
return Jtv
end

function NLPModels.jtprod_nln!(
nlp::AmplModel,
x::AbstractVector,
v::AbstractVector,
Jtv::AbstractVector,
)
nlp.counters.neval_jac_nln -= 1
nlp.counters.neval_jtprod_nln += 1
Jtv[1:(nlp.meta.nvar)] = jac_nln(nlp, Vector{Cdouble}(x))' * v
return Jtv
end

function NLPModels.hprod!(
nlp::AmplModel,
x::AbstractVector,
Expand Down