This PR allows the use to create QuadraticModels with dense H and A.
A few notes:
in the QPDataDense struct, M1 != M2 so that the user can provide special matrices such as Diagonal matrices,
NLPModel's functionhess_structure! and hess_coord! generate COO coefficients for a full matrix with zeros in the upper triangle in order to pass the tests in NLPModelsTest,
I did not implement specialized functions for QPDataDense when using SlackModel because it is supposed to create many zeros, so using SlackModel on this type of QPData converts it to a QPDataCOO,
I did not implement specialized functions for QPDataDense when using presolve, I suggest this is done in a separate PR (I can open an issue)
This PR allows the use to create QuadraticModels with dense
H
andA
. A few notes:QPDataDense
struct,M1 != M2
so that the user can provide special matrices such asDiagonal
matrices,hess_structure!
andhess_coord!
generate COO coefficients for a full matrix with zeros in the upper triangle in order to pass the tests in NLPModelsTest,QPDataDense
when usingSlackModel
because it is supposed to create many zeros, so usingSlackModel
on this type of QPData converts it to aQPDataCOO
,QPDataDense
when usingpresolve
, I suggest this is done in a separate PR (I can open an issue)