CoinMessageHandler * modelHandler_
Message handler for CbcModel.
double bestObj_
objValue of MIP
virtual void operator()(BabSetupBase *s)
operator() performs the branchAndBound
virtual void operator()(BabSetupBase &s)
operator() performs the branchAndBound
double continuousRelaxation()
returns the value of the continuous relaxation.
@ FeasibleOptimal
Optimum solution has been found and its optimality proved.
MipStatuses mipStatus() const
return Mip Status
int numNodes() const
return the total number of nodes explored.
double bestObj() const
return objective value of the bestSolution
(C) Copyright International Business Machines Corporation 2007
double * bestSolution_
Stores the solution of MIP.
OsiObject ** objects_
OsiObjects of the model.
A class to have all elements necessary to setup a branch-and-bound.
@ Feasible
An integer solution to the problem has been found.
int mipIterationCount_
get total number of iterations in last mip solved.
CbcModel model_
CbcModel used to solve problem.
double bestBound_
best known (lower) bound.
virtual void replaceIntegers(OsiObject **objects, int numberObjects)
virtual callback function to eventually modify objects for integer variable (replace with user set).
const CbcModel & model() const
Get cbc model used to solve.
const double * bestSolution() const
get the best solution known to the problem (is optimal if MipStatus is FeasibleOptimal).
int nObjects_
number of objects.
CbcModel & model()
Get cbc model used to solve as non-const, in case we want to change options before things happen.
@ NoSolutionKnown
No feasible solution to the problem is known.
virtual ~Bab()
destructor.
MipStatuses mipStatus_
Status of the mip solved.
double continuousRelaxation_
Continuous relaxation of the problem.
int iterationCount()
return the total number of iterations in the last mip solved.
int numNodes_
Number of nodes enumerated.
virtual void branchAndBound(BabSetupBase &s)
Perform a branch-and-bound using given setup.
MipStatuses
Integer optimization return codes.
double bestBound()
return the best known lower bound on the objective value
@ ProvenInfeasible
Problem has been proven to be infeasible.