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Simplify the implementation of jprod! #564

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18c6440
Simplify the implementation of jprod!
dpo Apr 17, 2026
b06bc34
Simplify cons! and jac_coord! accordingly
dpo Apr 17, 2026
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48 changes: 8 additions & 40 deletions src/nlp/api.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -61,20 +61,8 @@ function cons!(nlp::AbstractNLPModel, x::AbstractVector, cx::AbstractVector)
@lencheck get_nvar(nlp.meta) x
@lencheck get_ncon(nlp.meta) cx
increment!(nlp, :neval_cons)
if get_nlin(nlp.meta) > 0
if get_nnln(nlp.meta) == 0
cons_lin!(nlp, x, cx)
else
cons_lin!(nlp, x, view(cx, get_lin(nlp.meta)))
end
end
if get_nnln(nlp.meta) > 0
if get_nlin(nlp.meta) == 0
cons_nln!(nlp, x, cx)
else
cons_nln!(nlp, x, view(cx, get_nln(nlp.meta)))
end
end
get_nlin(nlp.meta) > 0 && cons_lin!(nlp, x, view(cx, get_lin(nlp.meta)))
get_nnln(nlp.meta) > 0 && cons_nln!(nlp, x, view(cx, get_nln(nlp.meta)))
Comment on lines +64 to +65

Copilot AI Apr 17, 2026

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cons! now always forwards cx to cons_lin!/cons_nln! as view(cx, meta.lin/meta.nln). When the problem is purely linear or purely nonlinear, this changes the argument type from the original cx (often a Vector/StridedVector) to a SubArray with index-vector indirection, which can break downstream model implementations that dispatch on Vector/StridedVector or use BLAS/external kernels requiring strided storage, and can add avoidable overhead. Consider restoring the fast-path branches for get_nnln(meta)==0 / get_nlin(meta)==0 (pass cx directly) and only using indexed views when both linear and nonlinear constraints are present.

Suggested change
get_nlin(nlp.meta) > 0 && cons_lin!(nlp, x, view(cx, get_lin(nlp.meta)))
get_nnln(nlp.meta) > 0 && cons_nln!(nlp, x, view(cx, get_nln(nlp.meta)))
nlin = get_nlin(nlp.meta)
nnln = get_nnln(nlp.meta)
if nnln == 0
cons_lin!(nlp, x, cx)
elseif nlin == 0
cons_nln!(nlp, x, cx)
else
cons_lin!(nlp, x, view(cx, get_lin(nlp.meta)))
cons_nln!(nlp, x, view(cx, get_nln(nlp.meta)))
end

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return cx
end

Expand Down Expand Up @@ -275,20 +263,12 @@ function jac_coord!(nlp::AbstractNLPModel, x::AbstractVector, vals::AbstractVect
@lencheck get_nnzj(nlp.meta) vals
increment!(nlp, :neval_jac)
if get_nlin(nlp.meta) > 0
if get_nnln(nlp.meta) == 0
jac_lin_coord!(nlp, x, vals)
else
lin_ind = 1:(get_lin_nnzj(nlp.meta))
jac_lin_coord!(nlp, x, view(vals, lin_ind))
end
lin_ind = 1:(get_lin_nnzj(nlp.meta))
jac_lin_coord!(nlp, x, view(vals, lin_ind))
end
if get_nnln(nlp.meta) > 0
if get_nlin(nlp.meta) == 0
jac_nln_coord!(nlp, x, vals)
else
nln_ind = (get_lin_nnzj(nlp.meta) + 1):(get_lin_nnzj(nlp.meta) + get_nln_nnzj(nlp.meta))
jac_nln_coord!(nlp, x, view(vals, nln_ind))
end
nln_ind = (get_lin_nnzj(nlp.meta) + 1):(get_lin_nnzj(nlp.meta) + get_nln_nnzj(nlp.meta))
jac_nln_coord!(nlp, x, view(vals, nln_ind))
Comment on lines 265 to +271

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jac_coord! now always passes view(vals, lin_ind/nln_ind) into jac_lin_coord!/jac_nln_coord! even when the Jacobian is entirely linear or entirely nonlinear. This changes the concrete type seen by model implementations from the original vals (commonly a Vector) to a SubArray, which may break downstream methods that dispatch on Vector/StridedVector or call routines requiring strided storage. Suggest keeping the previous single-block fast paths (call jac_*_coord! with vals directly when the other block is empty) and using views only for the mixed case.

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end
return vals
end
Expand Down Expand Up @@ -414,20 +394,8 @@ function jprod!(nlp::AbstractNLPModel, x::AbstractVector, v::AbstractVector, Jv:
@lencheck get_nvar(nlp.meta) x v
@lencheck get_ncon(nlp.meta) Jv
increment!(nlp, :neval_jprod)
if get_nlin(nlp.meta) > 0
if get_nnln(nlp.meta) == 0
jprod_lin!(nlp, x, v, Jv)
else
jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
end
end
if get_nnln(nlp.meta) > 0
if get_nlin(nlp.meta) == 0
jprod_nln!(nlp, x, v, Jv)
else
jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
end
end
get_nlin(nlp.meta) > 0 && jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
get_nnln(nlp.meta) > 0 && jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
Comment on lines +397 to +398

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jprod! now always calls jprod_lin!/jprod_nln! with view(Jv, ...). This removes the previous fast paths for the all-linear (get_nnln(meta)==0) and all-nonlinear (get_nlin(meta)==0) cases, and changes the type of the destination passed to downstream implementations from a plain Vector to a SubArray even when no remapping is needed. That can be a breaking change for model implementations that specialized jprod_lin!/jprod_nln! on concrete vectors (and it introduces extra indirection for the common contiguous/full-range case). Consider restoring the old branching (as done in cons! / jac_coord!) or conditionally passing Jv directly when the constraint set is purely linear or purely nonlinear.

Suggested change
get_nlin(nlp.meta) > 0 && jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
get_nnln(nlp.meta) > 0 && jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
if get_nnln(nlp.meta) == 0
jprod_lin!(nlp, x, v, Jv)
elseif get_nlin(nlp.meta) == 0
jprod_nln!(nlp, x, v, Jv)
else
jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
end

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Comment on lines +397 to +398

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There are no tests covering the edge cases where all constraints are linear (nnln==0) or all constraints are nonlinear (nlin==0). Since this change alters how jprod! dispatches (passing views vs the full vector), adding targeted tests for those two scenarios would help prevent regressions and catch downstream dispatch/type issues.

Suggested change
get_nlin(nlp.meta) > 0 && jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
get_nnln(nlp.meta) > 0 && jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
ncon = get_ncon(nlp.meta)
nlin = get_nlin(nlp.meta)
nnln = get_nnln(nlp.meta)
if nlin > 0
if nlin == ncon
jprod_lin!(nlp, x, v, Jv)
else
jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
end
end
if nnln > 0
if nnln == ncon
jprod_nln!(nlp, x, v, Jv)
else
jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
end
end

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Comment on lines +397 to +398

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jprod! now always calls jprod_lin!/jprod_nln! with view(Jv, meta.lin/meta.nln). For purely linear or purely nonlinear problems this changes the Jv argument from the original array (often a Vector/StridedVector) to an indexed SubArray, which can break downstream implementations that dispatch on Vector/StridedVector or use BLAS/external kernels, and may add overhead compared to the previous direct call. Consider reinstating the get_nnln(meta)==0 / get_nlin(meta)==0 fast paths (pass Jv directly) and only using indexed views when both constraint types are present.

Suggested change
get_nlin(nlp.meta) > 0 && jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
get_nnln(nlp.meta) > 0 && jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
if get_nnln(nlp.meta) == 0
jprod_lin!(nlp, x, v, Jv)
elseif get_nlin(nlp.meta) == 0
jprod_nln!(nlp, x, v, Jv)
else
jprod_lin!(nlp, x, v, view(Jv, get_lin(nlp.meta)))
jprod_nln!(nlp, x, v, view(Jv, get_nln(nlp.meta)))
end

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return Jv
end

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