Because of the linear API, the cost of the function jac_structure! is now prohibitive for large scale problems.
If we have as much linear constraints as nonlinear constraints, we need 2 * (ncon / 2)^2 comparisons (hidden in the calls to count).
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function jac_structure!( |
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nlp::AbstractNLPModel, |
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rows::AbstractVector{T}, |
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cols::AbstractVector{T}, |
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) where {T} |
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@lencheck nlp.meta.nnzj rows cols |
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lin_ind = 1:(nlp.meta.lin_nnzj) |
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if nlp.meta.nlin > 0 |
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if nlp.meta.nnln == 0 |
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jac_lin_structure!(nlp, rows, cols) |
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else |
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jac_lin_structure!(nlp, view(rows, lin_ind), view(cols, lin_ind)) |
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for i in lin_ind |
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rows[i] += count(x < nlp.meta.lin[rows[i]] for x in nlp.meta.nln) |
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end |
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end |
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end |
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if nlp.meta.nnln > 0 |
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if nlp.meta.nlin == 0 |
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jac_nln_structure!(nlp, rows, cols) |
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else |
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nln_ind = (nlp.meta.lin_nnzj + 1):(nlp.meta.lin_nnzj + nlp.meta.nln_nnzj) |
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jac_nln_structure!(nlp, view(rows, nln_ind), view(cols, nln_ind)) |
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for i in nln_ind |
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rows[i] += count(x < nlp.meta.nln[rows[i]] for x in nlp.meta.lin) |
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end |
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end |
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end |
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return rows, cols |
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end |
