inferGraphL2.py 文件源码

def Solve(self, M=Minimize, UseADMM=True, NumProcessors=0, Rho=1.0,
              MaxIters=250, EpsAbs=0.01, EpsRel=0.01, Verbose=False, 
              UseClustering = False, ClusterSize = 1000 ):
        global m_func
        m_func = M

        # Use ADMM if the appropriate parameter is specified and if there
        # are edges in the graph.
        #if __builtin__.len(SuperNodes) > 0:
        if UseClustering and ClusterSize > 0:
            SuperNodes = self.__ClusterGraph(ClusterSize)
            self.__SolveClusterADMM(M,UseADMM,SuperNodes, NumProcessors, Rho, MaxIters,\
                                     EpsAbs, EpsRel, Verbose)
            return
        if UseADMM and self.GetEdges() != 0:
            self.__SolveADMM(NumProcessors, Rho, MaxIters, EpsAbs, EpsRel,
                             Verbose)
            return
        if Verbose:
            print 'Serial ADMM'
        objective = 0
        constraints = []
        # Add all node objectives and constraints
        for ni in self.Nodes():
            nid = ni.GetId()
            objective += self.node_objectives[nid]
            constraints += self.node_constraints[nid]
        # Add all edge objectives and constraints
        for ei in self.Edges():
            etup = self.__GetEdgeTup(ei.GetSrcNId(), ei.GetDstNId())
            objective += self.edge_objectives[etup]
            constraints += self.edge_constraints[etup]
        # Solve CVXPY Problem
        objective = m_func(objective)
        problem = Problem(objective, constraints)
        try:
            problem.solve()
        except SolverError:
            problem.solve(solver=SCS)
        if problem.status in [INFEASIBLE_INACCURATE, UNBOUNDED_INACCURATE]:
            problem.solve(solver=SCS)
        # Set TGraphVX status and value to match CVXPY
        self.status = problem.status
        self.value = problem.value
        # Insert into hash to support ADMM structures and GetNodeValue()
        for ni in self.Nodes():
            nid = ni.GetId()
            variables = self.node_variables[nid]
            value = None
            for (varID, varName, var, offset) in variables:
                if var.size[0] == 1:
                    val = numpy.array([var.value])
                else:
                    val = numpy.array(var.value).reshape(-1,)
                if value is None:
                    value = val
                else:
                    value = numpy.concatenate((value, val))
            self.node_values[nid] = value