获取LP松弛解决方案值
为了在Pyscipopt进行优化后获得最终解决方案，我们可以
# define x to be a vector of x_ij variables
model.data = x
model.optimize()
X = model.getVal(x)
在分支和绑定树的每个节点上获取LP松弛解决方案。这样做的一种方法是使用model.getVal（t_x_ij）对每个（转换的）变量'x_ij'使用。是否有比在所有转换变量上循环的更有效的方法？ 如果您需要进一步的澄清，请告诉我。
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.
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如果要求解MIP，则需要在求解过程中获得LP解决方案值。您需要在解决新节点LP时实现一个回调。
您可能需要查看 treed ，我创建了一个项目，用于检查和可视化各种LP相关的信息。
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.