Add "mixed" standard form representation #3201
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| @@ -372,7 +384,30 @@ def write(self, model): | |||
| f"model contains a trivially infeasible constraint, '{con.name}'" | |||
| ) | |||
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| if slack_form: | |||
| if mixed_form: | |||
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Is it more likely that a user will want mixed_form over slack_form?
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Users will probably do the regular form first, and then maybe the slack form. However, I am working on some (more efficient) solver interfaces (a rewrite of gurobi_direct) that will use the mixed_form.
| # Note that the solution is a mix of inequality and equality constraints | ||
| # self._verify_solution(soln, repn, False) |
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Not debugging: a breadcrumb as to why that test is not an exact copy-and-paste of the cases above. I have expanded the comment.
| active_var_idx[-1] = A.indptr[-1] | ||
| # active_var_idx[-1] = len(columns) | ||
| A = scipy.sparse.csc_array( |
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| ) | ||
| self.assertTrue(np.all(repn.A == ref)) | ||
| self.assertTrue(np.all(repn.b == np.array([3, 5, 6, -3, 8]))) | ||
| self.assertTrue(np.all(repn.c == np.array([[-1, 0, -5, 0], [1, 0, 0, 15]]))) |
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EEEEEK, I didn't notice this before--should we really let a "standard form" have more than one objective? This caused me a moment of terror on the the thought that I would definitely assume c is a vector of the same dimension as the number of columns in A... The matrix c makes sense to me from a writer perspective. From a user/transformation perspective, we're always going to need to be checking that this isn't the case... But I guess we have to do that anyway, so why not here?
Fixes # .
Summary/Motivation:
This adds a new option to the "Standard Form" compiler that returns a mix of inequality and equality constraints. This representation is particularly useful for solver interfaces. This PR also resolves an error when collapsing unused variables out of the returned matrices. The new implementation is also slightly more performant.
Changes proposed in this PR:
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