Add an API for n-th order tensor products #486
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src/nlp/api.jl
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| """ | ||
| P = tensor_projection(nlp, n, x, directions, args...) | ||
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| Returns the projection of the n-th derivative of the objective of `nlp` at `x` along the specified directions. |
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What about tensor_projection ?
You should also have
P = tensor_projection(nlp, n, x, y, directions, args...)
as the default, where by default
P = tensor_projection(nlp, n, x, directions, args...)
call the first one with zeros(n).
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I agree that it will be great to have tensor products with both the objective and the Lagrangian.
Should we also add an API for just the constraints, or include a scaling factor \alpha that can go in front of the objective?
\alpha can be zero.
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I think it makes sense to be coherent with the other functions like hprod.
src/nlp/api.jl
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| ) where {T, S} | ||
| @lencheck nlp.meta.nvar x | ||
| m = n - length(directions) | ||
| @assert m ≥ 1 |
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Can we use NLPModels.@rangecheck instead of @Assert ?
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Can we use rangecheck to ensure inequalities?
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Not tested, but I would expect @rangecheck 1 typemax(Int) m to work
cc @michel2323