Use MOI.Nonlinear.QPBlockData - #274
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August 13, 2026 10:24
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Replace the objective union field, the four vectors of affine and quadratic constraints, and their bespoke evaluation helpers (_fill_gradient, _fill_jacobian, _fill_result) with a single MOI.Nonlinear.QPBlockData, which also answers the per-constraint-type MOI attribute queries. The equality and inequality callbacks evaluate the block once and scatter the rows into NLopt's separate equality and inequality blocks.
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* Use MOI.Nonlinear.ModelWithQuad instead of QPBlockData Store the affine and quadratic objective and constraints in a MOI.Nonlinear.ModelWithQuad whose inner model is the nonlinear model, and build a single MOI.Nonlinear.EvaluatorWithQuad in optimize! that stacks the quadratic rows before the NLPBlock rows. The equality/inequality classification and the Jacobian assembly are now uniform over all rows, which removes the separate QP scatter machinery (qp_eq_pos/qp_le_pos) and the direct use of the private product functions of QPBlockData. * MOI * Set the quadratic objective with Nonlinear.set_objective MOI.Nonlinear.ModelWithQuad no longer implements MOI.set for the objective; Nonlinear.set_objective is the single way to set it. * Merge the variables and the nonlinear model into one field MOI.Nonlinear.ModelWithQuad now owns the variables (with indices guaranteed to be 1:n), so the variables, quad_data and nlp_model fields collapse into a single model field. A uses_nlp_block flag replaces the nlp_model !== nothing test to tell the legacy MOI.NLPBlock API apart, because optimize! overwrites nlp_data; the NLPBlock is rebuilt on every solve unless that flag is set, so that a stale objective cannot survive an objective switch.
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A net line deletion of 134 lines. Needs