Objective sensitivity for Conic and Quadratic #330
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blegat
reviewed
Dec 2, 2025
| end | ||
| dd += df_dx * val | ||
| else | ||
| error( |
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If you use
elseif df_dx isa MOI.AbstractFunction
MOI.Utilities.eval_variables(variable -> MOI.get(model, MOI.VariablePrimal(), term.variable), model, df_dx) * val
endThen it will also work for nonlinear
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But it will be quite inefficient to use that for loop for nonlinear. In that case, we should do reverse diff instead. But then, if the code only works for quadratic, we probably shouldn't do MOI.Nonlinear.SymbolicAD.derivative(obj_func, xi) and we should directly specialize it for the ScalarQuadraticFunction looping over the quadratic and linear terms.
blegat
reviewed
Dec 2, 2025
| xj_val = MOI.get(model, MOI.VariablePrimal(), term.variable) | ||
| dd += term.coefficient * xj_val * val | ||
| end | ||
| dd += df_dx * val |
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