OSBonminSolver.cpp

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#include <iostream>


#include "OSBonminSolver.h"
#include "OSDataStructures.h"
#include "OSParameters.h" 
#include "OSCommonUtil.h"
#include "OSMathUtil.h"

#include "BonOsiTMINLPInterface.hpp"
#include "BonCbc.hpp"

#include "CoinTime.hpp"

using std::cout; 
using std::endl; 
using std::ostringstream;


BonminSolver::BonminSolver() {
        osrlwriter = new OSrLWriter();
        osresult = new OSResult();
        m_osilreader = NULL;
        m_osolreader = NULL;
        bonminErrorMsg = "";

} 

BonminSolver::~BonminSolver() {
        #ifdef DEBUG
        cout << "inside BonminSolver destructor" << endl;
        #endif
        if(m_osilreader != NULL) delete m_osilreader;
        m_osilreader = NULL;
        if(m_osolreader != NULL) delete m_osolreader;
        m_osolreader = NULL;
        delete osresult;
        osresult = NULL;
        delete osrlwriter;
        osrlwriter = NULL;
        //delete osinstance;
        //osinstance = NULL;
        #ifdef DEBUG
        cout << "leaving BonminSolver destructor" << endl;
        #endif
}






bool BonminProblem::get_variables_types(Index n, VariableType* var_types){
        int i = 0;
        char *varType;
        varType = osinstance->getVariableTypes();
        n = osinstance->getVariableNumber();
        for(i = 0; i < n;   i++){
                if( varType[i] == 'B') {
                        var_types[i] = BINARY;
                }
                else{
                        if( varType[i] == 'I') {
                                var_types[i] = INTEGER;
                        }
                        else{
                                var_types[i] = CONTINUOUS;
                        }       
                }
        }
        return true;
}

bool BonminProblem::get_variables_linearity(Index n, Ipopt::TNLP::LinearityType* var_types){
        
        
        
        std::cout << "Initialize Nonlinear Structures" << std::endl;
        try{
                osinstance->initForAlgDiff( );
        }
        catch(const ErrorClass& eclass){
                bonminErrorMsg = eclass.errormsg;
                throw;  
        }       
        std::map<int, int> varIndexMap;
        std::map<int, int>::iterator posVarIndexMap;
        varIndexMap = osinstance->getAllNonlinearVariablesIndexMap( );
        /* first make all continuous */
        
        int i;
        
        for(i = 0; i < n; i++){
                var_types[ i] = Ipopt::TNLP::LINEAR;    
        }
        for(posVarIndexMap = varIndexMap.begin(); posVarIndexMap != varIndexMap.end(); ++posVarIndexMap){
                        std::cout <<  "Variable Index = "   << posVarIndexMap->first  << std::endl ;
                        var_types[ posVarIndexMap->first] = Ipopt::TNLP::NON_LINEAR;
        }
        std::cout << "Number of nonlinear variables =  " << varIndexMap.size() << std::endl;    

        return true;
}

bool BonminProblem::get_constraints_linearity(Index m, Ipopt::TNLP::LinearityType* const_types){
        
        int i;
        
        for(i  = 0; i < m; i++){
                const_types[ i] = Ipopt::TNLP::LINEAR;
        }
        
        
        int mm = osinstance->getNumberOfNonlinearExpressionTreeModIndexes();
        

        
        for(i = 0; i < mm; i++){
                if(osinstance->getNonlinearExpressionTreeModIndexes()[ i] >= 0){
                        std::cout << osinstance->getNonlinearExpressionTreeModIndexes()[ i] << std::endl;
                        const_types[ osinstance->getNonlinearExpressionTreeModIndexes()[ i] ] = Ipopt::TNLP::NON_LINEAR;
                        
                }
                
        }
                
        return true;
}

// returns the size of the problem
bool BonminProblem::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
                             Index& nnz_h_lag, TNLP::IndexStyleEnum& index_style)
{
        if(osinstance->getObjectiveNumber() <= 0) throw ErrorClass("Bonmin NEEDS AN OBJECTIVE FUNCTION");                       
        // number of variables
        n = osinstance->getVariableNumber();
        // number of constraints
        m = osinstance->getConstraintNumber();
        cout << "Bonmin number variables  !!!!!!!!!!!!!!!!!!!!!!!!!!!" << n << endl;
        cout << "Bonmin number constraints  !!!!!!!!!!!!!!!!!!!!!!!!!!!" << m << endl;
        try{
                osinstance->initForAlgDiff( );
        }
        catch(const ErrorClass& eclass){
                bonminErrorMsg = eclass.errormsg;
                throw;  
        }       
        // use the OS Expression tree for function evaluations instead of CppAD
        osinstance->bUseExpTreeForFunEval = true;
        //std::cout << "Call sparse jacobian" << std::endl;
        SparseJacobianMatrix *sparseJacobian = NULL;
        try{
                sparseJacobian = osinstance->getJacobianSparsityPattern();
        }
        catch(const ErrorClass& eclass){
                bonminErrorMsg = eclass.errormsg;
                throw;  
        }
        //std::cout << "Done calling sparse jacobian" << std::endl;
        nnz_jac_g = sparseJacobian->valueSize;
        cout << "nnz_jac_g  !!!!!!!!!!!!!!!!!!!!!!!!!!!" << nnz_jac_g << endl;  
        // nonzeros in upper hessian
        
        if( (osinstance->getNumberOfNonlinearExpressions() == 0) && (osinstance->getNumberOfQuadraticTerms() == 0) ) {
                cout << "This is a linear program"  << endl;
                nnz_h_lag = 0;
        }
        else{
                //std::cout << "Get Lagrangian Hessian Sparsity Pattern " << std::endl;
                SparseHessianMatrix *sparseHessian = osinstance->getLagrangianHessianSparsityPattern();
                //std::cout << "Done Getting Lagrangian Hessian Sparsity Pattern " << std::endl;
                nnz_h_lag = sparseHessian->hessDimension;
        }
        cout << "print nnz_h_lag (OSBonminSolver.cpp)" << endl; 
        cout << "nnz_h_lag  !!!!!!!!!!!!!!!!!!!!!!!!!!!" << nnz_h_lag << endl;  
        cout << "set index_style (OSBonminSolver.cpp)" << endl; 
        // use the C style indexing (0-based)
        index_style = TNLP::C_STYLE;
        cout << "return from get_nlp_info (OSBonminSolver.cpp)" << endl;        
  

  return true;
}//get_nlp_info


bool  BonminProblem::get_bounds_info(Index n, Number* x_l, Number* x_u,
                                Index m, Number* g_l, Number* g_u){
        int i; 

        double * mdVarLB = osinstance->getVariableLowerBounds();
        //std::cout << "GET BOUNDS INFORMATION FOR BONMIN !!!!!!!!!!!!!!!!!" << std::endl;
        // variables upper bounds
        double * mdVarUB = osinstance->getVariableUpperBounds();

        for(i = 0; i < n; i++){
                x_l[ i] = mdVarLB[ i];
                x_u[ i] = mdVarUB[ i];
                cout << "x_l !!!!!!!!!!!!!!!!!!!!!!!!!!!" << x_l[i] << endl;
                cout << "x_u !!!!!!!!!!!!!!!!!!!!!!!!!!!" << x_u[i] << endl;
        }
        // Bonmin interprets any number greater than nlp_upper_bound_inf as
        // infinity. The default value of nlp_upper_bound_inf and nlp_lower_bound_inf
        // is 1e19 and can be changed through bonmin options.
        // e.g. g_u[0] = 2e19;

        //constraint lower bounds
        double * mdConLB = osinstance->getConstraintLowerBounds();
        //constraint upper bounds
        double * mdConUB = osinstance->getConstraintUpperBounds();
        
        for(int i = 0; i < m; i++){
                g_l[ i] = mdConLB[ i];
                g_u[ i] = mdConUB[ i];
                cout << "lower !!!!!!!!!!!!!!!!!!!!!!!!!!!" << g_l[i] << endl;
                cout << "upper !!!!!!!!!!!!!!!!!!!!!!!!!!!" << g_u[i] << endl;
        }  
        return true;
}//get_bounds_info


// returns the initial point for the problem
bool BonminProblem::get_starting_point(Index n, bool init_x, Number* x,
     bool init_z, Number* z_L, Number* z_U, Index m, bool init_lambda,
     Number* lambda) {
        // Here, we assume we only have starting values for x, if you code
        // your own NLP, you can provide starting values for the dual variables
        // if you wish
        assert(init_x == true);
        assert(init_z == false);
        assert(init_lambda == false);
        int i, m1, n1;

#ifdef DEBUG
        cout << "get initial values !!!!!!!!!!!!!!!!!!!!!!!!!! " << endl;
#endif
        
        //now set initial values
#ifdef DEBUG
        cout << "get number of initial values !!!!!!!!!!!!!!!!!!!!!!!!!! " << endl;
        cout << "Is osoption = NULL? " << (osoption == NULL) << endl;
#endif
        int k;
        if (osoption != NULL)
                m1 = osoption->getNumberOfInitVarValues();
        else
                m1 = 0;
#ifdef DEBUG
        cout << "number of variables initialed: " << m1 << endl;
#endif

        n1 = osinstance->getVariableNumber();
        bool* initialed;
        initialed = new bool[n1];
#ifdef DEBUG
        cout << "number of variables in total: " << n1 << endl;
#endif


        for(k = 0; k < n1; k++)
                initialed[k] = false;

        if (m1 > 0)
        {
#ifdef DEBUG
                        cout << "get initial values " << endl;
#endif

                InitVarValue**  initVarVector = osoption->getInitVarValuesSparse();
#ifdef DEBUG
                cout << "done " << endl;
#endif

                double initval;
                try 
                {
                        for(k = 0; k < m1; k++)
                        {       
                                i = initVarVector[k]->idx;
                                if (initVarVector[k]->idx > n1)
                                        throw ErrorClass ("Illegal index value in variable initialization");

                                initval = initVarVector[k]->value;
                                if (osinstance->instanceData->variables->var[i]->ub == OSDBL_MAX)
                                {       if (osinstance->instanceData->variables->var[i]->lb > initval)
                                                throw ErrorClass ("Initial value outside of bounds");
                                }
                                else
                                        if (osinstance->instanceData->variables->var[i]->lb == -OSDBL_MAX)
                                        {       if (osinstance->instanceData->variables->var[i]->ub < initval)
                                                        throw ErrorClass ("Initial value outside of bounds");
                                        }
                                        else
                                        {       if ((osinstance->instanceData->variables->var[i]->lb > initval) ||
                                                        (osinstance->instanceData->variables->var[i]->ub < initval))
                                                        throw ErrorClass ("Initial value outside of bounds");
                                        }

                                x[initVarVector[k]->idx] = initval;
                                initialed[initVarVector[k]->idx] = true;
                        }
                }
                catch(const ErrorClass& eclass)
                {       cout << "Error in BonminProblem::get_starting_point (OSBonminSolver.cpp)";
                        cout << endl << endl << endl;
                }       
        }  //  end if (m1 > 0)          

        double default_initval;
        default_initval = 1.7171;

        for(k = 0; k < n1; k++)
        {
                if (!initialed[k])
                        if (osinstance->instanceData->variables->var[k]->ub == OSDBL_MAX)
                                if (osinstance->instanceData->variables->var[k]->lb <= default_initval)
                                        x[k] = default_initval;
                                else
                                        x[k] = osinstance->instanceData->variables->var[k]->lb;
                        else
                                if (osinstance->instanceData->variables->var[k]->lb == -OSDBL_MAX)
                                        if (osinstance->instanceData->variables->var[k]->ub >= default_initval)
                                                x[k] = default_initval;
                                        else
                                                x[k] = osinstance->instanceData->variables->var[k]->ub;
                                else
                                        if ((osinstance->instanceData->variables->var[k]->lb <= default_initval) && 
                                                (osinstance->instanceData->variables->var[k]->ub >= default_initval))
                                                x[k] = default_initval;
                                        else
                                                if (osinstance->instanceData->variables->var[k]->lb > default_initval)
                                                        x[k] = osinstance->instanceData->variables->var[k]->lb;
                                                else
                                                        x[k] = osinstance->instanceData->variables->var[k]->ub;
        }
        for(i = 0; i < n1; i++){
                std::cout << "INITIAL VALUE !!!!!!!!!!!!!!!!!!!!  " << x[ i] << std::endl;
        }
 

        delete[] initialed;
        
        return true;
}//get_starting_point


// returns the value of the objective function
bool BonminProblem::eval_f(Index n, const Number* x, bool new_x, Number& obj_value){
        try{
                obj_value  = osinstance->calculateAllObjectiveFunctionValues( const_cast<double*>(x), NULL, NULL, new_x, 0 )[ 0];
        }
        catch(const ErrorClass& eclass){
                bonminErrorMsg = eclass.errormsg;
                throw;  
        }
        if( CommonUtil::ISOSNAN( (double)obj_value) ) return false;
        return true;
}

bool BonminProblem::eval_grad_f(Index n, const Number* x, bool new_x, Number* grad_f){
        int i;
        double *objGrad;
        try{
                //objGrad = osinstance->calculateAllObjectiveFunctionGradients( const_cast<double*>(x), NULL, NULL,  new_x, 1)[ 0];
                //std::cout << "Calculate Objective function gradient " << std::endl;
                // we assume we are doing the objective function indexed by -1
                objGrad = osinstance->calculateObjectiveFunctionGradient( const_cast<double*>(x), NULL, NULL, -1,  new_x, 1);
        }
        catch(const ErrorClass& eclass){
                bonminErrorMsg = eclass.errormsg;
                throw;  
        }
        for(i = 0; i < n; i++){
                grad_f[ i]  = objGrad[ i];
                //std::cout << grad_f[ i]  << std::endl;
        }
        std::cout << "DONE WITH Calculate Objective function gradient " << std::endl;
        return true;
}//eval_grad_f

// return the value of the constraints: g(x)
bool BonminProblem::eval_g(Index n, const Number* x, bool new_x, Index m, Number* g) {
        try{
                double *conVals = osinstance->calculateAllConstraintFunctionValues( const_cast<double*>(x), NULL, NULL, new_x, 0 );
                int i;
                for(i = 0; i < m; i++){
                        if( CommonUtil::ISOSNAN( (double)conVals[ i] ) ) return false;
                        g[i] = conVals[ i]  ;   
                } 
                return true;
        }
        catch(const ErrorClass& eclass){
                bonminErrorMsg = eclass.errormsg;
                throw;  
        }
}//eval_g


// return the structure or values of the jacobian
bool BonminProblem::eval_jac_g(Index n, const Number* x, bool new_x,
                           Index m, Index nele_jac, Index* iRow, Index *jCol,
                           Number* values){
        SparseJacobianMatrix *sparseJacobian;
        if (values == NULL) {
                // return the values of the jacobian of the constraints
                //cout << "n: " << n << endl;
                //cout << "m: " << m << endl;
                //cout << "nele_jac: " <<  nele_jac << endl;
                // return the structure of the jacobian
                try{
                        sparseJacobian = osinstance->getJacobianSparsityPattern();
                }
                catch(const ErrorClass& eclass){
                        bonminErrorMsg =  eclass.errormsg; 
                        throw; 
                }
                int i = 0;
                int k, idx;
                for(idx = 0; idx < m; idx++){
                        for(k = *(sparseJacobian->starts + idx); k < *(sparseJacobian->starts + idx + 1); k++){
                                iRow[i] = idx;
                                jCol[i] = *(sparseJacobian->indexes + k);
                                //cout << "ROW IDX  !!!!!!!!!!!!!!!!!!!!!!!!!!!"  << iRow[i] << endl;
                                //cout << "COL IDX  !!!!!!!!!!!!!!!!!!!!!!!!!!!"  << jCol[i] << endl;
                                i++;
                        }
                }
        }
        else {
                //std::cout << "EVALUATING JACOBIAN" << std::endl; 
                try{
                        sparseJacobian = osinstance->calculateAllConstraintFunctionGradients( const_cast<double*>(x), NULL, NULL,  new_x, 1);
                }
                catch(const ErrorClass& eclass){
                        bonminErrorMsg = eclass.errormsg;
                        throw;  
                }
                //osinstance->getIterateResults( (double*)x, 0.0, NULL, -1, new_x,  1);
                for(int i = 0; i < nele_jac; i++){
                        values[ i] = sparseJacobian->values[i];
                        //values[ i] = osinstance->m_mdJacValue[ i];
                        //cout << "values[i]:!!!!!!!!!!!!  " <<  values[ i] << endl;
                        //cout << "m_mdJacValue[ i]:!!!!!!!!!!!!  " <<  osinstance->m_mdJacValue[ i] << endl;
                }
        }
  return true;
}//eval_jac_g

//return the structure or values of the hessian
bool BonminProblem::eval_h(Index n, const Number* x, bool new_x,
                       Number obj_factor, Index m, const Number* lambda,
                       bool new_lambda, Index nele_hess, Index* iRow,
                       Index* jCol, Number* values){

        SparseHessianMatrix *sparseHessian;
        
        int i;
        if (values == NULL) {
                // return the structure. This is a symmetric matrix, fill the lower left triangle only.
                //cout << "get structure of HESSIAN !!!!!!!!!!!!!!!!!!!!!!!!!! "  << endl;
                try{
                        sparseHessian = osinstance->getLagrangianHessianSparsityPattern( );
                }
                catch(const ErrorClass& eclass){
                        bonminErrorMsg = eclass.errormsg;
                        throw;  
                }
                //cout << "got structure of HESSIAN !!!!!!!!!!!!!!!!!!!!!!!!!! "  << endl;
                for(i = 0; i < nele_hess; i++){
                        iRow[i] = *(sparseHessian->hessColIdx + i);
                        jCol[i] = *(sparseHessian->hessRowIdx + i);
                                //cout << "ROW HESS IDX  !!!!!!!!!!!!!!!!!!!!!!!!!!!"  << iRow[i] << endl;
                                //cout << "COL HESS IDX  !!!!!!!!!!!!!!!!!!!!!!!!!!!"  << jCol[i] << endl;
                }
        }
        else {
                //std::cout << "EVALUATING HESSIAN" << std::endl; 
                // return the values. This is a symmetric matrix, fill the lower left triangle only
                double* objMultipliers = new double[1];
                objMultipliers[0] = obj_factor;
                try{
                        sparseHessian = osinstance->calculateLagrangianHessian( const_cast<double*>(x), objMultipliers, const_cast<double*>(lambda) ,  new_x, 2);
                delete[]  objMultipliers;
                }
                catch(const ErrorClass& eclass){
                        bonminErrorMsg = eclass.errormsg;
                        delete[]  objMultipliers;
                        throw;  
                }
                for(i = 0; i < nele_hess; i++){
                        values[ i]  = *(sparseHessian->hessValues + i);
                }
        }
        return true;
}//eval_h

bool BonminProblem::get_scaling_parameters(Number& obj_scaling,
                        bool& use_x_scaling, Index n,
                        Number* x_scaling,
                    bool& use_g_scaling, Index m,
                    Number* g_scaling){
        /*
         * Bonmin assumes problems cast as a min
         * if( osinstance->instanceData->objectives->obj[ 0]->maxOrMin.compare("min") != 0){
                obj_scaling = -1;
        }
    else obj_scaling = 1;
    use_x_scaling = false;
    use_g_scaling = false;
    */
        return true;
}//get_scaling_parameters




void
BonminProblem::finalize_solution(TMINLP::SolverReturn status,
                            Index n, const Number* x, Number obj_value)
{
        OSrLWriter *osrlwriter ;
        osrlwriter = new OSrLWriter();
        std::cout<<"Problem status: "<<status<<std::endl;
        std::cout<<"Objective value: "<<obj_value<<std::endl;
        if(printSol_ && x != NULL){
                std::cout<<"Solution:"<<std::endl;
                for(int i = 0 ; i < n ; i++){
                        std::cout<<"x["<<i<<"] = "<<x[i];
                        if(i < n-1) std::cout<<", ";
                }
                std::cout<<std::endl;
        }
        
          printf("\n\nObjective value\n");
          printf("f(x*) = %e\n", obj_value);
        int solIdx = 0;
        ostringstream outStr;
        double* mdObjValues = new double[1];
        std::string message = "Bonmin solver finishes to the end.";
        std::string solutionDescription = "";   

        try{
                
                // resultHeader information
                if(osresult->setServiceName( "Bonmin solver service") != true)
                        throw ErrorClass("OSResult error: setServiceName");
                if(osresult->setInstanceName(  osinstance->getInstanceName()) != true)
                        throw ErrorClass("OSResult error: setInstanceName");

                //if(osresult->setJobID( osoption->jobID) != true)
                //      throw ErrorClass("OSResult error: setJobID");

                // set basic problem parameters
                if(osresult->setVariableNumber( osinstance->getVariableNumber()) != true)
                        throw ErrorClass("OSResult error: setVariableNumer");
                if(osresult->setObjectiveNumber( 1) != true)
                        throw ErrorClass("OSResult error: setObjectiveNumber");
                if(osresult->setConstraintNumber( osinstance->getConstraintNumber()) != true)
                        throw ErrorClass("OSResult error: setConstraintNumber");
                if(osresult->setSolutionNumber(  1) != true)
                        throw ErrorClass("OSResult error: setSolutionNumer");   


                if(osresult->setGeneralMessage( message) != true)
                        throw ErrorClass("OSResult error: setGeneralMessage");

                switch( status){
                        case SUCCESS:
                                solutionDescription = "SUCCESS[BONMIN]: Algorithm terminated successfully at a locally optimal point, satisfying the convergence tolerances.";
                                osresult->setSolutionStatus(solIdx,  "locallyOptimal", solutionDescription);
                                osresult->setPrimalVariableValues(solIdx, const_cast<double*>(x), osinstance->getVariableNumber());
                                //osresult->setDualVariableValues(solIdx, const_cast<double*>( lambda));
                                mdObjValues[0] = obj_value;
                                osresult->setObjectiveValues(solIdx, mdObjValues, 1);
                        break;
                        case MAXITER_EXCEEDED:
                                solutionDescription = "MAXITER_EXCEEDED[BONMIN]: Maximum number of iterations exceeded.";
                                osresult->setSolutionStatus(solIdx,  "stoppedByLimit", solutionDescription);
                                osresult->setPrimalVariableValues(solIdx, const_cast<double*>(x), osinstance->getVariableNumber());
                                //osresult->setDualVariableValues(solIdx, const_cast<double*>( lambda));
                                mdObjValues[0] = obj_value;
                                osresult->setObjectiveValues(solIdx, mdObjValues, 1);
                        break;
                        case STOP_AT_TINY_STEP:
                                solutionDescription = "STOP_AT_TINY_STEP[BONMIN]: Algorithm proceeds with very little progress.";
                                osresult->setSolutionStatus(solIdx,  "stoppedByLimit", solutionDescription);
                                osresult->setPrimalVariableValues(solIdx, const_cast<double*>( x), osinstance->getVariableNumber());
                                //osresult->setDualVariableValues(solIdx, const_cast<double*>( lambda));
                                mdObjValues[0] = obj_value;
                                osresult->setObjectiveValues(solIdx, mdObjValues, 1);
                        break;
                        case STOP_AT_ACCEPTABLE_POINT:
                                solutionDescription = "STOP_AT_ACCEPTABLE_POINT[BONMIN]: Algorithm stopped at a point that was converged, not to _desired_ tolerances, but to _acceptable_ tolerances";
                                osresult->setSolutionStatus(solIdx,  "BonminAccetable", solutionDescription);
                                osresult->setPrimalVariableValues(solIdx, const_cast<double*>(x), osinstance->getVariableNumber());
                                //osresult->setDualVariableValues(solIdx, const_cast<double*>( lambda));
                                mdObjValues[0] = obj_value;
                                osresult->setObjectiveValues(solIdx, mdObjValues, 1);
                        break;
                        case LOCAL_INFEASIBILITY:
                                solutionDescription = "LOCAL_INFEASIBILITY[BONMIN]: Algorithm converged to a point of local infeasibility. Problem may be infeasible.";
                                osresult->setSolutionStatus(solIdx,  "infeasible", solutionDescription);
                        break;
                        case USER_REQUESTED_STOP:
                                solutionDescription = "USER_REQUESTED_STOP[BONMIN]: The user call-back function  intermediate_callback returned false, i.e., the user code requested a premature termination of the optimization.";
                                osresult->setSolutionStatus(solIdx,  "error", solutionDescription);
                        break;
                        case DIVERGING_ITERATES:
                                solutionDescription = "DIVERGING_ITERATES[BONMIN]: It seems that the iterates diverge.";
                                osresult->setSolutionStatus(solIdx,  "unbounded", solutionDescription);
                        break;
                        case RESTORATION_FAILURE:
                                solutionDescription = "RESTORATION_FAILURE[BONMIN]: Restoration phase failed, algorithm doesn't know how to proceed.";
                                osresult->setSolutionStatus(solIdx,  "error", solutionDescription);
                        break;
                        case ERROR_IN_STEP_COMPUTATION:
                                solutionDescription = "ERROR_IN_STEP_COMPUTATION[BONMIN]: An unrecoverable error occurred while IPOPT tried to compute the search direction.";
                                osresult->setSolutionStatus(solIdx,  "error", solutionDescription);
                        break;
                        case INVALID_NUMBER_DETECTED:
                                solutionDescription = "INVALID_NUMcatBER_DETECTED[BONMIN]: Algorithm received an invalid number (such as NaN or Inf) from the NLP; see also option check_derivatives_for_naninf.";
                                osresult->setSolutionStatus(solIdx,  "error", solutionDescription);
                        break;
                        case INTERNAL_ERROR:
                                solutionDescription = "INTERNAL_ERROR[BONMIN]: An unknown internal error occurred. Please contact the IPOPT authors through the mailing list.";
                                osresult->setSolutionStatus(solIdx,  "error", solutionDescription);
                        break;
                        default:
                                solutionDescription = "OTHER[BONMIN]: other unknown solution status from Bonmin solver";
                                osresult->setSolutionStatus(solIdx,  "other", solutionDescription);
                }
                osresult->setGeneralStatusType("normal");
                delete osrlwriter;
                delete[] mdObjValues;
                osrlwriter = NULL;

        }
        
        catch(const ErrorClass& eclass){
                osresult->setGeneralMessage( eclass.errormsg);
                osresult->setGeneralStatusType( "error");
                std::string osrl = osrlwriter->writeOSrL( osresult);
                delete osrlwriter;
                osrlwriter = NULL;
                throw ErrorClass(  osrl) ;
                delete[] mdObjValues;
                mdObjValues = NULL;
        }
}


   

void BonminSolver::buildSolverInstance() throw (ErrorClass) {
        try{            
                if(osil.length() == 0 && osinstance == NULL) throw ErrorClass("there is no instance");
                if(osinstance == NULL){
                        m_osilreader = new OSiLReader();
                        osinstance = m_osilreader->readOSiL( osil);
                }
                // Create a new instance of your nlp 
                tminlp = new BonminProblem( osinstance, osoption, osresult);
                this->bCallbuildSolverInstance = true;
                //Now initialize from tminlp
                bonmin.initialize( GetRawPtr(tminlp) );
        }
        catch(const ErrorClass& eclass){
                std::cout << "THERE IS AN ERROR" << std::endl;
                osresult->setGeneralMessage( eclass.errormsg);
                osresult->setGeneralStatusType( "error");
                osrl = osrlwriter->writeOSrL( osresult);
                throw ErrorClass( osrl) ;
        }                               
}//end buildSolverInstance() 



void BonminSolver::setSolverOptions() throw (ErrorClass) {
        try{

                this->bSetSolverOptions = true;
                bonmin.initializeOptionsAndJournalist();
                //Register an additional option
                bonmin.roptions()->AddStringOption2("print_solution","Do we print the solution or not?",
                                                 "yes",
                                                 "no", "No, we don't.",
                                                 "yes", "Yes, we do.",
                                                 "A longer comment can be put here");
                  
                 // Here we can change the default value of some Bonmin or Ipopt option
                bonmin.options()->SetNumericValue("bonmin.time_limit", 1000); //changes bonmin's time limit
                bonmin.options()->SetStringValue("mu_oracle","loqo");
                  
                //Here we read several option files
                bonmin.readOptionsFile("Mybonmin.opt");
                bonmin.readOptionsFile();// This reads the default file "bonmin.opt"
                  
                // Options can also be set by using a string with a format similar to the bonmin.opt file
                bonmin.readOptionsString("bonmin.algorithm B-BB\n");
                  
                // Now we can obtain the value of the new option
                int printSolution;
                bonmin.options()->GetEnumValue("print_solution", printSolution,"");
                if(printSolution == 1){
                        tminlp->printSolutionAtEndOfAlgorithm();
                }       
                
                if(osoption == NULL && osol.length() > 0)
                {
                        m_osolreader = new OSoLReader();
                        osoption = m_osolreader->readOSoL( osol);
                }

                if(osoption != NULL){
                        std::cout << "number of solver options "  <<  osoption->getNumberOfSolverOptions() << std::endl;
                        std::vector<SolverOption*> optionsVector;
                        optionsVector = osoption->getSolverOptions( "bonmin");
                        char *pEnd;
                        int i;
                        int num_bonmin_options = optionsVector.size();
                        for(i = 0; i < num_bonmin_options; i++){
                                std::cout << "bonmin solver option  "  << optionsVector[ i]->name << std::endl;
                                if(optionsVector[ i]->type == "numeric" ){
                                        std::cout << "FOUND A NUMERIC OPTION  "  <<  os_strtod( optionsVector[ i]->value.c_str(), &pEnd ) << std::endl;
                                        bonmin.options()->SetNumericValue(optionsVector[ i]->name, os_strtod( optionsVector[ i]->value.c_str(), &pEnd ) );      
                                }
                                else if(optionsVector[ i]->type == "integer" ){
                                        std::cout << "FOUND AN INTEGER OPTION  "  << atoi( optionsVector[ i]->value.c_str() ) << std::endl;
                                        bonmin.options()->SetIntegerValue(optionsVector[ i]->name, atoi( optionsVector[ i]->value.c_str() ) );
                                }
                                else if(optionsVector[ i]->type == "string" ){
                                        bonmin.options()->SetStringValue(optionsVector[ i]->name, optionsVector[ i]->value);
                                }
                        }       
                }
                
        }
        
        catch(const ErrorClass& eclass){
                std::cout << "THERE IS AN ERROR" << std::endl;
                osresult->setGeneralMessage( eclass.errormsg);
                osresult->setGeneralStatusType( "error");
                osrl = osrlwriter->writeOSrL( osresult);
                throw ErrorClass( osrl) ;
        }                               
}//end setSolverOptions() 


//void BonminSolver::solve() throw (ErrorClass) {
void BonminSolver::solve() throw (ErrorClass) {
        if( this->bCallbuildSolverInstance == false) buildSolverInstance();
        std::cout << "set Solver Options for Gus" << std::endl;
        if( this->bSetSolverOptions == false) setSolverOptions();
        std::cout << "done setting set Solver Options for Gus" << std::endl;

        try{
                double start = CoinCpuTime();
                //OSiLWriter osilwriter;
                //cout << osilwriter.writeOSiL( osinstance) << endl;
                if(osinstance->getVariableNumber() <= 0)throw ErrorClass("Bonmin requires decision variables");

//              The next line should come out eventually...
                if(osinstance->getConstraintNumber() <= 0)throw ErrorClass("Bonmin cannot handle unconstrained problems");

                double duration = CoinCpuTime() - start;
                cout << "Parsing took (seconds): "<< duration << endl;

                  try {
                    Bab bb;
                    bb(  bonmin);  //process parameter file using Ipopt and do branch and bound using Cbc


                  }
                  catch(TNLPSolver::UnsolvedError *E) {
                    //There has been a failure to solve a problem with Ipopt.
                    std::cerr<<"Ipopt has failed to solve a problem"<<std::endl;
                  }
                  catch(OsiTMINLPInterface::SimpleError &E) {
                    std::cerr<<E.className()<<"::"<<E.methodName()
                             <<std::endl
                             <<E.message()<<std::endl;
                  }
                  catch(CoinError &E) {
                    std::cerr<<E.className()<<"::"<<E.methodName()
                             <<std::endl
                             <<E.message()<<std::endl;
                  }
                

                // see if we have a linear program
                if(osinstance->getObjectiveNumber() <= 0) throw ErrorClass("Bonmin NEEDS AN OBJECTIVE FUNCTION");
                if( (osinstance->getNumberOfNonlinearExpressions() == 0) && (osinstance->getNumberOfQuadraticTerms() == 0) ) 
                        //app->Options()->SetStringValue("hessian_approximation", "limited-memory");
                // if it is a max problem call scaling and set to -1
                if( osinstance->instanceData->objectives->obj[ 0]->maxOrMin.compare("min") != 0){
                        //app->Options()->SetStringValue("nlp_scaling_method", "user-scaling");
                }

                std::cout << "Finish Bonmin Optimize" << std::endl;
                osrl = osrlwriter->writeOSrL( osresult);
                std::cout << "Finish writing the osrl" << std::endl;


        }
        catch(const ErrorClass& eclass){
                osresult->setGeneralMessage( eclass.errormsg);
                osresult->setGeneralStatusType( "error");
                osrl = osrlwriter->writeOSrL( osresult);
                throw ErrorClass( osrl) ;
        }
}//solve


void BonminSolver::dataEchoCheck(){
        
        int i;
        
        // print out problem parameters
        cout << "This is problem:  " << osinstance->getInstanceName() << endl;
        cout << "The problem source is:  " << osinstance->getInstanceSource() << endl;
        cout << "The problem description is:  " << osinstance->getInstanceDescription() << endl;
        cout << "number of variables = " << osinstance->getVariableNumber() << endl;
        cout << "number of Rows = " << osinstance->getConstraintNumber() << endl;

        // print out the variable information
        if(osinstance->getVariableNumber() > 0){
                for(i = 0; i < osinstance->getVariableNumber(); i++){
                        if(osinstance->getVariableNames() != NULL) cout << "variable Names  " << osinstance->getVariableNames()[ i]  << endl;
                        if(osinstance->getVariableTypes() != NULL) cout << "variable Types  " << osinstance->getVariableTypes()[ i]  << endl;
                        if(osinstance->getVariableLowerBounds() != NULL) cout << "variable Lower Bounds  " << osinstance->getVariableLowerBounds()[ i]  << endl;
                        if(osinstance->getVariableUpperBounds() != NULL) cout << "variable Upper Bounds  " <<  osinstance->getVariableUpperBounds()[i] << endl;
                }
        }
        
        // print out objective function information
        if(osinstance->getVariableNumber() > 0 || osinstance->instanceData->objectives->obj != NULL || osinstance->instanceData->objectives->numberOfObjectives > 0){
                if( osinstance->getObjectiveMaxOrMins()[0] == "min")  cout <<  "problem is a minimization" << endl;
                else cout <<  "problem is a maximization" << endl;
                for(i = 0; i < osinstance->getVariableNumber(); i++){
                        cout << "OBJ COEFFICIENT =  " <<  osinstance->getDenseObjectiveCoefficients()[0][i] << endl;
                }
        }
        // print out constraint information
        if(osinstance->getConstraintNumber() > 0){
                for(i = 0; i < osinstance->getConstraintNumber(); i++){
                        if(osinstance->getConstraintNames() != NULL) cout << "row name = " << osinstance->getConstraintNames()[i] <<  endl;
                        if(osinstance->getConstraintLowerBounds() != NULL) cout << "row lower bound = " << osinstance->getConstraintLowerBounds()[i] <<  endl;
                        if(osinstance->getConstraintUpperBounds() != NULL) cout << "row upper bound = " << osinstance->getConstraintUpperBounds()[i] <<  endl; 
                }
        }
        
        // print out linear constraint data
        cout << endl;
        cout << "number of nonzeros =  " << osinstance->getLinearConstraintCoefficientNumber() << endl;
        for(i = 0; i <= osinstance->getVariableNumber(); i++){
                cout << "Start Value =  " << osinstance->getLinearConstraintCoefficientsInColumnMajor()->starts[ i] << endl;
        }
        cout << endl;
        for(i = 0; i < osinstance->getLinearConstraintCoefficientNumber(); i++){
                cout << "Index Value =  " << osinstance->getLinearConstraintCoefficientsInColumnMajor()->indexes[i] << endl;
                cout << "Nonzero Value =  " << osinstance->getLinearConstraintCoefficientsInColumnMajor()->values[i] << endl;
        }

        // print out quadratic data
        cout << "number of qterms =  " <<  osinstance->getNumberOfQuadraticTerms() << endl;
        for(int i = 0; i <  osinstance->getNumberOfQuadraticTerms(); i++){
                cout << "Row Index = " <<  osinstance->getQuadraticTerms()->rowIndexes[i] << endl;
                cout << "Var Index 1 = " << osinstance->getQuadraticTerms()->varOneIndexes[ i] << endl;
                cout << "Var Index 2 = " << osinstance->getQuadraticTerms()->varTwoIndexes[ i] << endl;
                cout << "Coefficient = " << osinstance->getQuadraticTerms()->coefficients[ i] << endl;
        }
} // end dataEchoCheck


BonminProblem::BonminProblem(OSInstance *osinstance_,  OSOption *osoption_, OSResult *osresult_) {
        osinstance = osinstance_;
        osoption = osoption_;
        osresult = osresult_;
        printSol_ = false;
}

BonminProblem::~BonminProblem() {

}