CVXPY
Minimize
  Sum(x, None, False)
Subject To
 126: [[np.float64(0.0) np.float64(1.0)]
 [np.float64(2.0) np.float64(3.0)]] @ x + y + Promote(1.0, (2,)) == 0.0
 131: y == 0.0
Bounds
 x free
 y free
End
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AFTER COMPILATION
OBJECTIVE
  Sense            : minimize
VARIABLES
  [continuous] <x_0>: obj=1, original bounds=[-inf,+inf]
  [continuous] <x_1>: obj=1, original bounds=[-inf,+inf]
  [continuous] <x_2>: obj=0, original bounds=[-inf,+inf]
  [continuous] <x_3>: obj=0, original bounds=[-inf,+inf]
CONSTRAINTS
  [linear] <c1>: <x_1>[C] +<x_2>[C] == -1;
  [linear] <c2>:  +2<x_0>[C] +3<x_1>[C] +<x_3>[C] == -1;
  [linear] <c3>: <x_2>[C] == 0;
  [linear] <c4>: <x_3>[C] == 0;
END
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SCIP
OBJECTIVE
  Sense            : minimize
VARIABLES
  [continuous] <x_0>: obj=1, original bounds=[-inf,+inf]
  [continuous] <x_1>: obj=1, original bounds=[-inf,+inf]
  [continuous] <y_0>: obj=0, original bounds=[-inf,+inf]
  [continuous] <y_1>: obj=0, original bounds=[-inf,+inf]
CONSTRAINTS
  [linear] <126_0>: <x_1>[C] +<y_0>[C] == -1;
  [linear] <126_1>:  +2<x_0>[C] +3<x_1>[C] +<y_1>[C] == -1;
  [linear] <131_0>: <y_0>[C] == 0;
  [linear] <131_1>: <y_1>[C] == 0;
END