To get the final solution after optimization in pyscipopt, we can do
# define x to be a vector of x_ij variables
model.data = x
model.optimize()
X = model.getVal(x)
I would like to get the LP relaxation solutions at every node of the branch and bound tree. One method for doing this would be to use model.getVal(t_x_ij)
for every (transformed) variable 'x_ij'. Is there a more efficient way of doing this than looping over all the transformed variables? Please let me know if you need any further clarifications.
If you are solving a MIP, you would need to get the LP solution values during the solving process. You need to implement a callback that is executed whenever a new node LP is solved.
You might want to check out TreeD , a project I created to inspect and visualize various LP-related information during the MIP solving process of PySCIPOpt .
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