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I have used CVXPY and some of its LP solvers to determine whether a solution to an A*x <= b problem is feasible, and now I would like to try PySCIPOpt. I could not find an example of this in the docs, and I'm having trouble figuring out the right syntax. With CVXPY the code is simply:

def do_cvxpy(A, b, solver):
    x = cvxpy.Variable(A.shape[1])
    constraints = [A@x <= b] #The @ denotes matrix multiplication in CVXPY
    obj = cvxpy.Minimize(0)
    prob = cvxpy.Problem(obj, constraints)
    prob.solve(solver=solver)
    return prob.status

I think with PySCIPOpt one cannot use matrix notation as above, but must treat vectors and matrices as collections of scalar variables, each of which has to be added individually, so I tried this:

def do_scip(A, b):
    model = Model("XYZ")
    x = {}
    for i in range(A.shape[1]):
        x[i] = model.addVar(vtype="C", name="x(%s)" % i)
        
    model.setObjective(0)  #Is this right for a feasibility-only problem?
    model.addCons(A*x <= b) #This is certainly the wrong syntax
    model.optimize()
    return model.getStatus()

Could anyone please help me out with the correct form for the constraint in addCons() for this kind of problem, and confirm that an acceptable way to ask whether a solution is feasible is to simply pass 0 as the objective?

kevinafra
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1 Answers1

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I'm still not positive about the setObjective(0), but at least I can get the code to run without errors by "unpacking" the A matrix and the b vector and adding each element as a constraint:

for i in range(ncols):
    for j in range(nrows):
        model.addCons(A[j,i]*x[i] <= b[i])

I also discovered that CVXPY actually has an interface to SCIP, but it gives me an error when I try to use it:

getSolObjVal cannot only be called in stage SOLVING without a valid solution

which seems to suggest that the interface cannot be used for feasibility-only problems.

kevinafra
  • 11
  • 3