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Add error for evaluation of constraints of an unconstrained model #465

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d31171c
Add error for evaluation of constraints of an unconstrained model
tmigot Jun 18, 2024
8901300
Fix unconstrained error
tmigot Jun 22, 2024
c89cb94
fix julia 1.6
tmigot Jun 22, 2024
21cb8aa
does not have any
tmigot Jun 24, 2024
6f53439
Merge branch 'main' into error-unconstrained
tmigot Aug 12, 2024
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23 changes: 23 additions & 0 deletions src/nlp/api.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -45,6 +45,7 @@ Evaluate ``c(x)``, the constraints at `x`.
"""
function cons(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x
check_unconstrained(nlp)
c = S(undef, nlp.meta.ncon)
return cons!(nlp, x, c)
end
Expand All @@ -70,6 +71,7 @@ Evaluate the linear constraints at `x`.
"""
function cons_lin(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x
check_linear_constraints(nlp)
c = S(undef, nlp.meta.nlin)
return cons_lin!(nlp, x, c)
end
Expand All @@ -88,6 +90,7 @@ Evaluate the nonlinear constraints at `x`.
"""
function cons_nln(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x
check_nonlinear_constraints(nlp)
c = S(undef, nlp.meta.nnln)
return cons_nln!(nlp, x, c)
end
Expand Down Expand Up @@ -118,6 +121,7 @@ Evaluate ``f(x)`` and ``c(x)`` at `x`.
"""
function objcons(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x
check_unconstrained(nlp)
f = obj(nlp, x)
c = cons(nlp, x)
return f, c
Expand All @@ -131,6 +135,7 @@ Evaluate ``f(x)`` and ``c(x)`` at `x`. `c` is overwritten with the value of ``c(
function objcons!(nlp::AbstractNLPModel, x::AbstractVector, c::AbstractVector)
@lencheck nlp.meta.nvar x
@lencheck nlp.meta.ncon c
check_unconstrained(nlp)
f = obj(nlp, x)
cons!(nlp, x, c)
return f, c
Expand Down Expand Up @@ -166,6 +171,7 @@ end
Return the structure of the constraints Jacobian in sparse coordinate format.
"""
function jac_structure(nlp::AbstractNLPModel)
check_unconstrained(nlp)
rows = Vector{Int}(undef, nlp.meta.nnzj)
cols = Vector{Int}(undef, nlp.meta.nnzj)
jac_structure!(nlp, rows, cols)
Expand All @@ -181,6 +187,7 @@ function jac_structure!(
rows::AbstractVector{T},
cols::AbstractVector{T},
) where {T}
check_unconstrained(nlp)
@lencheck nlp.meta.nnzj rows cols
lin_ind = 1:(nlp.meta.lin_nnzj)
nlp.meta.nlin > 0 && jac_lin_structure!(nlp, view(rows, lin_ind), view(cols, lin_ind))
Expand All @@ -203,6 +210,7 @@ end
Return the structure of the linear constraints Jacobian in sparse coordinate format.
"""
function jac_lin_structure(nlp::AbstractNLPModel)
check_linear_constraints(nlp)
rows = Vector{Int}(undef, nlp.meta.lin_nnzj)
cols = Vector{Int}(undef, nlp.meta.lin_nnzj)
jac_lin_structure!(nlp, rows, cols)
Expand All @@ -221,6 +229,7 @@ function jac_lin_structure! end
Return the structure of the nonlinear constraints Jacobian in sparse coordinate format.
"""
function jac_nln_structure(nlp::AbstractNLPModel)
check_nonlinear_constraints(nlp)
rows = Vector{Int}(undef, nlp.meta.nln_nnzj)
cols = Vector{Int}(undef, nlp.meta.nln_nnzj)
jac_nln_structure!(nlp, rows, cols)
Expand All @@ -242,6 +251,7 @@ rewriting `vals`.
function jac_coord!(nlp::AbstractNLPModel, x::AbstractVector, vals::AbstractVector)
@lencheck nlp.meta.nvar x
@lencheck nlp.meta.nnzj vals
check_unconstrained(nlp)
increment!(nlp, :neval_jac)
lin_ind = 1:(nlp.meta.lin_nnzj)
nlp.meta.nlin > 0 && jac_lin_coord!(nlp, x, view(vals, lin_ind))
Expand All @@ -257,6 +267,7 @@ Evaluate ``J(x)``, the constraints Jacobian at `x` in sparse coordinate format.
"""
function jac_coord(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x
check_unconstrained(nlp)
vals = S(undef, nlp.meta.nnzj)
return jac_coord!(nlp, x, vals)
end
Expand All @@ -268,6 +279,7 @@ Evaluate ``J(x)``, the constraints Jacobian at `x` as a sparse matrix.
"""
function jac(nlp::AbstractNLPModel, x::AbstractVector)
@lencheck nlp.meta.nvar x
check_unconstrained(nlp)
rows, cols = jac_structure(nlp)
vals = jac_coord(nlp, x)
sparse(rows, cols, vals, nlp.meta.ncon, nlp.meta.nvar)
Expand All @@ -288,6 +300,7 @@ Evaluate ``J(x)``, the linear constraints Jacobian at `x` in sparse coordinate f
"""
function jac_lin_coord(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x
check_linear_constraints(nlp)
vals = S(undef, nlp.meta.lin_nnzj)
return jac_lin_coord!(nlp, x, vals)
end
Expand All @@ -299,6 +312,7 @@ Evaluate ``J(x)``, the linear constraints Jacobian at `x` as a sparse matrix.
"""
function jac_lin(nlp::AbstractNLPModel, x::AbstractVector)
@lencheck nlp.meta.nvar x
check_linear_constraints(nlp)
rows, cols = jac_lin_structure(nlp)
vals = jac_lin_coord(nlp, x)
sparse(rows, cols, vals, nlp.meta.nlin, nlp.meta.nvar)
Expand All @@ -319,6 +333,7 @@ Evaluate ``J(x)``, the nonlinear constraints Jacobian at `x` in sparse coordinat
"""
function jac_nln_coord(nlp::AbstractNLPModel{T, S}, x::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x
check_nonlinear_constraints(nlp)
vals = S(undef, nlp.meta.nln_nnzj)
return jac_nln_coord!(nlp, x, vals)
end
Expand All @@ -330,6 +345,7 @@ Evaluate ``J(x)``, the nonlinear constraints Jacobian at `x` as a sparse matrix.
"""
function jac_nln(nlp::AbstractNLPModel, x::AbstractVector)
@lencheck nlp.meta.nvar x
check_nonlinear_constraints(nlp)
rows, cols = jac_nln_structure(nlp)
vals = jac_nln_coord(nlp, x)
sparse(rows, cols, vals, nlp.meta.nnln, nlp.meta.nvar)
Expand All @@ -342,6 +358,7 @@ Evaluate ``J(x)v``, the Jacobian-vector product at `x`.
"""
function jprod(nlp::AbstractNLPModel{T, S}, x::AbstractVector, v::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x v
check_unconstrained(nlp)
Jv = S(undef, nlp.meta.ncon)
return jprod!(nlp, x, v, Jv)
end
Expand All @@ -354,6 +371,7 @@ Evaluate ``J(x)v``, the Jacobian-vector product at `x` in place.
function jprod!(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector, Jv::AbstractVector)
@lencheck nlp.meta.nvar x v
@lencheck nlp.meta.ncon Jv
check_unconstrained(nlp)
increment!(nlp, :neval_jprod)
nlp.meta.nlin > 0 && jprod_lin!(nlp, x, v, view(Jv, nlp.meta.lin))
nlp.meta.nnln > 0 && jprod_nln!(nlp, x, v, view(Jv, nlp.meta.nln))
Expand All @@ -377,6 +395,7 @@ function jprod!(
@lencheck nlp.meta.nnzj rows cols vals
@lencheck nlp.meta.nvar v
@lencheck nlp.meta.ncon Jv
check_unconstrained(nlp)
increment!(nlp, :neval_jprod)
coo_prod!(rows, cols, vals, v, Jv)
end
Expand All @@ -388,6 +407,7 @@ Evaluate ``J(x)v``, the linear Jacobian-vector product at `x`.
"""
function jprod_lin(nlp::AbstractNLPModel{T, S}, x::AbstractVector, v::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x v
check_linear_constraints(nlp)
Jv = S(undef, nlp.meta.nlin)
return jprod_lin!(nlp, x, v, Jv)
end
Expand Down Expand Up @@ -416,6 +436,7 @@ function jprod_lin!(
@lencheck nlp.meta.lin_nnzj rows cols vals
@lencheck nlp.meta.nvar v
@lencheck nlp.meta.nlin Jv
check_linear_constraints(nlp)
increment!(nlp, :neval_jprod_lin)
coo_prod!(rows, cols, vals, v, Jv)
end
Expand All @@ -427,6 +448,7 @@ Evaluate ``J(x)v``, the nonlinear Jacobian-vector product at `x`.
"""
function jprod_nln(nlp::AbstractNLPModel{T, S}, x::AbstractVector, v::AbstractVector) where {T, S}
@lencheck nlp.meta.nvar x v
check_nonlinear_constraints(nlp)
Jv = S(undef, nlp.meta.nnln)
return jprod_nln!(nlp, x, v, Jv)
end
Expand Down Expand Up @@ -455,6 +477,7 @@ function jprod_nln!(
@lencheck nlp.meta.nln_nnzj rows cols vals
@lencheck nlp.meta.nvar v
@lencheck nlp.meta.nnln Jv
check_nonlinear_constraints(nlp)
increment!(nlp, :neval_jprod_nln)
coo_prod!(rows, cols, vals, v, Jv)
end
Expand Down
24 changes: 24 additions & 0 deletions src/nlp/utils.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -67,6 +67,30 @@ macro rangecheck(lo, hi, vars...)
Expr(:block, exprs...)
end

const UnconstrainedErrorMessage = "Try to evaluate constraints, but the problem is unconstrained."

function check_unconstrained(nlp)
if unconstrained(nlp)
throw(error(UnconstrainedErrorMessage))
end
end

const NonlinearUnconstrainedErrorMessage = "Try to evaluate nonlinear constraints, but the problem has none."

function check_nonlinear_constraints(nlp)
if nlp.meta.nnln == 0
throw(error(NonlinearUnconstrainedErrorMessage))
end
end

const LinearUnconstrainedErrorMessage = "Try to evaluate linear constraints, but the problem has none."

function check_linear_constraints(nlp)
if nlp.meta.nlin == 0
throw(error(LinearUnconstrainedErrorMessage))
end
end

"""
coo_prod!(rows, cols, vals, v, Av)

Expand Down
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