Exemple #1
0
FX FX::operator[](int k) const {

  // Argument checking
  if (k<0) k+=getNumOutputs();
  casadi_assert_message(k<getNumOutputs(),"FX[int k]:: Attempt to select the k'th output with k=" << k << ", but should be smaller or equal to number of outputs (" << getNumOutputs() << ").");
  
  // Get the inputs in MX form
  std::vector< MX > in = symbolicInput();
  
  // Clone such that we can use const.
  FX clone = *this;
  
  // Get the outputs
  std::vector< MX > result = clone.call(in);
  
  // Construct an MXFunction with only the k'th output
  MXFunction ret(in,result[k]);
  
  ret.setInputScheme(getInputScheme());
  
  // Initialize it
  ret.init();
  
  // And return it, will automatically cast to FX
  return ret;
}
Exemple #2
0
void EvaluationMX::evaluateMX(const MXPtrV& arg, MXPtrV& res, const MXPtrVV& fseed, MXPtrVV& fsens, const MXPtrVV& aseed, MXPtrVV& asens, bool output_given) {
  
  // Number of sensitivity directions
  int nfdir = fsens.size();
  casadi_assert(nfdir==0 || fcn_.spCanEvaluate(true));
  int nadir = aseed.size();
  casadi_assert(nadir==0 || fcn_.spCanEvaluate(false));

  // Get/generate the derivative function
  FX d = fcn_.derivative(nfdir, nadir);

  // Temporary
  vector<MX> tmp;

  // Assemble inputs
  vector<MX> d_arg;
  d_arg.reserve(d.getNumInputs());

  // Nondifferentiated inputs
  tmp = getVector(arg);
  d_arg.insert(d_arg.end(), tmp.begin(), tmp.end());
  for (MXPtrVV::const_iterator i = fseed.begin(); i != fseed.end(); ++i) {
    tmp = getVector(*i);
    d_arg.insert(d_arg.end(), tmp.begin(), tmp.end());
  }
  for (MXPtrVV::const_iterator i = aseed.begin(); i != aseed.end(); ++i) {
    tmp = getVector(*i);
    d_arg.insert(d_arg.end(), tmp.begin(), tmp.end());
  }

  // Evaluate symbolically
  vector<MX> d_res = d.call(d_arg);
  vector<MX>::const_iterator d_res_it = d_res.begin();

  // Collect the nondifferentiated results
  for (MXPtrV::iterator i = res.begin(); i != res.end(); ++i, ++d_res_it) {
    if (!output_given && *i) **i = *d_res_it;
  }

  // Collect the forward sensitivities
  for (MXPtrVV::iterator j = fsens.begin(); j != fsens.end(); ++j) {
    for (MXPtrV::iterator i = j->begin(); i != j->end(); ++i, ++d_res_it) {
      if (*i) **i = *d_res_it;
    }
  }

  // Collect the adjoint sensitivities
  for (MXPtrVV::iterator j = asens.begin(); j != asens.end(); ++j) {
    for (MXPtrV::iterator i = j->begin(); i != j->end(); ++i, ++d_res_it) {
      if(*i && !d_res_it->isNull()){
        **i += *d_res_it;
      }
    }
  }

  // Make sure that we've got to the end of the outputs
  casadi_assert(d_res_it==d_res.end());
}
MXFunction vec (const FX &a_) {
  FX a = a_;

  // Pass null if input is null
  if (a.isNull()) return MXFunction();
  
  // Get the MX inputs, only used for shape
  const std::vector<MX> &symbolicInputMX = a.symbolicInput();
  // Have a vector with MX that have the shape of vec(symbolicInputMX )
  std::vector<MX> symbolicInputMX_vec(a.getNumInputs());
  // Make vector valued MX's out of them
  std::vector<MX> symbolicInputMX_vec_reshape(a.getNumInputs());

  // Apply the vec-transformation to the inputs
  for (int i=0;i<symbolicInputMX.size();++i) {
    std::stringstream s;
    s << "X_flat_" << i;
    symbolicInputMX_vec[i] = MX(s.str(),vec(symbolicInputMX[i].sparsity()));
    symbolicInputMX_vec_reshape[i] = trans(reshape(symbolicInputMX_vec[i],trans(symbolicInputMX[i].sparsity())));
  }
  
  // Call the original function with the vecced inputs
  std::vector<MX> symbolicOutputMX = a.call(symbolicInputMX_vec_reshape);
  
  // Apply the vec-transformation to the outputs
  for (int i=0;i<symbolicOutputMX.size();++i)
    symbolicOutputMX[i] = vec(symbolicOutputMX[i]);
    
  // Make a new function with the vecced input/outputs
  MXFunction ret(symbolicInputMX_vec,symbolicOutputMX);
  
  // Initialize it if a was
  if (a.isInit()) ret.init();
  return ret;

}  
void NLPSolverInternal::init(){
  // Read options
  verbose_ = getOption("verbose");
  gauss_newton_ = getOption("gauss_newton");
  
  // Initialize the functions
  casadi_assert_message(!F_.isNull(),"No objective function");
  if(!F_.isInit()){
    F_.init();
    log("Objective function initialized");
  }
  if(!G_.isNull() && !G_.isInit()){
    G_.init();
    log("Constraint function initialized");
  }

  // Get dimensions
  n_ = F_.input(0).numel();
  m_ = G_.isNull() ? 0 : G_.output(0).numel();

  parametric_ = getOption("parametric");
  
  if (parametric_) {
    casadi_assert_message(F_.getNumInputs()==2, "Wrong number of input arguments to F for parametric NLP. Must be 2, but got " << F_.getNumInputs());
  } else {
    casadi_assert_message(F_.getNumInputs()==1, "Wrong number of input arguments to F for non-parametric NLP. Must be 1, but got " << F_.getNumInputs() << " instead. Do you perhaps intend to use fixed parameters? Then use the 'parametric' option.");
  }

  // Basic sanity checks
  casadi_assert_message(F_.getNumInputs()==1 || F_.getNumInputs()==2, "Wrong number of input arguments to F. Must be 1 or 2");
  
  if (F_.getNumInputs()==2) parametric_=true;
  casadi_assert_message(getOption("ignore_check_vec") || gauss_newton_ || F_.input().size2()==1,
     "To avoid confusion, the input argument to F must be vector. You supplied " << F_.input().dimString() << endl <<
     " We suggest you make the following changes:" << endl <<
     "   -  F is an SXFunction:  SXFunction([X],[rhs]) -> SXFunction([vec(X)],[rhs])" << endl <<
     "             or            F -                   ->  F = vec(F) " << 
     "   -  F is an MXFunction:  MXFunction([X],[rhs]) -> " <<  endl <<
     "                                     X_vec = MX(\"X\",vec(X.sparsity())) " << endl <<
     "                                     F_vec = MXFunction([X_flat],[F.call([X_flat.reshape(X.sparsity())])[0]]) " << endl <<
     "             or            F -                   ->  F = vec(F) " << 
     " You may ignore this warning by setting the 'ignore_check_vec' option to true." << endl
  );
  
  casadi_assert_message(F_.getNumOutputs()>=1, "Wrong number of output arguments to F");
  casadi_assert_message(gauss_newton_  || F_.output().scalar(), "Output argument of F not scalar.");
  casadi_assert_message(F_.output().dense(), "Output argument of F not dense.");
  casadi_assert_message(F_.input().dense(), "Input argument of F must be dense. You supplied " << F_.input().dimString());
  
  if(!G_.isNull()) {
    if (parametric_) {
      casadi_assert_message(G_.getNumInputs()==2, "Wrong number of input arguments to G for parametric NLP. Must be 2, but got " << G_.getNumInputs());
    } else {
      casadi_assert_message(G_.getNumInputs()==1, "Wrong number of input arguments to G for non-parametric NLP. Must be 1, but got " << G_.getNumInputs() << " instead. Do you perhaps intend to use fixed parameters? Then use the 'parametric' option.");
    }
    casadi_assert_message(G_.getNumOutputs()>=1, "Wrong number of output arguments to G");
    casadi_assert_message(G_.input().numel()==n_, "Inconsistent dimensions");
    casadi_assert_message(G_.input().sparsity()==F_.input().sparsity(), "F and G input dimension must match. F " << F_.input().dimString() << ". G " << G_.input().dimString());
  }
  
  // Find out if we are to expand the objective function in terms of scalar operations
  bool expand_f = getOption("expand_f");
  if(expand_f){
    log("Expanding objective function");
    
    // Cast to MXFunction
    MXFunction F_mx = shared_cast<MXFunction>(F_);
    if(F_mx.isNull()){
      casadi_warning("Cannot expand objective function as it is not an MXFunction");
    } else {
      // Take use the input scheme of G if possible (it might be an SXFunction)
      vector<SXMatrix> inputv;
      if(!G_.isNull() && F_.getNumInputs()==G_.getNumInputs()){
        inputv = G_.symbolicInputSX();
      } else {
        inputv = F_.symbolicInputSX();
      }
      
      // Try to expand the MXFunction
      F_ = F_mx.expand(inputv);
      F_.setOption("number_of_fwd_dir",F_mx.getOption("number_of_fwd_dir"));
      F_.setOption("number_of_adj_dir",F_mx.getOption("number_of_adj_dir"));
      F_.init();
    }
  }
  
  
  // Find out if we are to expand the constraint function in terms of scalar operations
  bool expand_g = getOption("expand_g");
  if(expand_g){
    log("Expanding constraint function");
    
    // Cast to MXFunction
    MXFunction G_mx = shared_cast<MXFunction>(G_);
    if(G_mx.isNull()){
      casadi_warning("Cannot expand constraint function as it is not an MXFunction");
    } else {
      // Take use the input scheme of F if possible (it might be an SXFunction)
      vector<SXMatrix> inputv;
      if(F_.getNumInputs()==G_.getNumInputs()){
        inputv = F_.symbolicInputSX();
      } else {
        inputv = G_.symbolicInputSX();
      }
      
      // Try to expand the MXFunction
      G_ = G_mx.expand(inputv);
      G_.setOption("number_of_fwd_dir",G_mx.getOption("number_of_fwd_dir"));
      G_.setOption("number_of_adj_dir",G_mx.getOption("number_of_adj_dir"));
      G_.init();
    }
  }
  
  // Find out if we are to expand the constraint function in terms of scalar operations
  bool generate_hessian = getOption("generate_hessian");
  if(generate_hessian && H_.isNull()){
    casadi_assert_message(!gauss_newton_,"Automatic generation of Gauss-Newton Hessian not yet supported");
    log("generating hessian");
    
    // Simple if unconstrained
    if(G_.isNull()){
      // Create Hessian of the objective function
      FX HF = F_.hessian();
      HF.init();
      
      // Symbolic inputs of HF
      vector<MX> HF_in = F_.symbolicInput();
      
      // Lagrange multipliers
      MX lam("lam",0);
      
      // Objective function scaling
      MX sigma("sigma");
      
      // Inputs of the Hessian function
      vector<MX> H_in = HF_in;
      H_in.insert(H_in.begin()+1, lam);
      H_in.insert(H_in.begin()+2, sigma);

      // Get an expression for the Hessian of F
      MX hf = HF.call(HF_in).at(0);
      
      // Create the scaled Hessian function
      H_ = MXFunction(H_in, sigma*hf);
      log("Unconstrained Hessian function generated");
      
    } else { // G_.isNull()
      
      // Check if the functions are SXFunctions
      SXFunction F_sx = shared_cast<SXFunction>(F_);
      SXFunction G_sx = shared_cast<SXFunction>(G_);
      
      // Efficient if both functions are SXFunction
      if(!F_sx.isNull() && !G_sx.isNull()){
        // Expression for f and g
        SXMatrix f = F_sx.outputSX();
        SXMatrix g = G_sx.outputSX();
        
        // Numeric hessian
        bool f_num_hess = F_sx.getOption("numeric_hessian");
        bool g_num_hess = G_sx.getOption("numeric_hessian");
        
        // Number of derivative directions
        int f_num_fwd = F_sx.getOption("number_of_fwd_dir");
        int g_num_fwd = G_sx.getOption("number_of_fwd_dir");
        int f_num_adj = F_sx.getOption("number_of_adj_dir");
        int g_num_adj = G_sx.getOption("number_of_adj_dir");
        
        // Substitute symbolic variables in f if different input variables from g
        if(!isEqual(F_sx.inputSX(),G_sx.inputSX())){
          f = substitute(f,F_sx.inputSX(),G_sx.inputSX());
        }
        
        // Lagrange multipliers
        SXMatrix lam = ssym("lambda",g.size1());

        // Objective function scaling
        SXMatrix sigma = ssym("sigma");        
        
        // Lagrangian function
        vector<SXMatrix> lfcn_in(parametric_? 4: 3);
        lfcn_in[0] = G_sx.inputSX();
        lfcn_in[1] = lam;
        lfcn_in[2] = sigma;
        if (parametric_) lfcn_in[3] = G_sx.inputSX(1);
        SXFunction lfcn(lfcn_in, sigma*f + inner_prod(lam,g));
        lfcn.setOption("verbose",getOption("verbose"));
        lfcn.setOption("numeric_hessian",f_num_hess || g_num_hess);
        lfcn.setOption("number_of_fwd_dir",std::min(f_num_fwd,g_num_fwd));
        lfcn.setOption("number_of_adj_dir",std::min(f_num_adj,g_num_adj));
        lfcn.init();
        
        // Hessian of the Lagrangian
        H_ = static_cast<FX&>(lfcn).hessian();
        H_.setOption("verbose",getOption("verbose"));
        log("SX Hessian function generated");
        
      } else { // !F_sx.isNull() && !G_sx.isNull()
        // Check if the functions are SXFunctions
        MXFunction F_mx = shared_cast<MXFunction>(F_);
        MXFunction G_mx = shared_cast<MXFunction>(G_);
        
        // If they are, check if the arguments are the same
        if(!F_mx.isNull() && !G_mx.isNull() && isEqual(F_mx.inputMX(),G_mx.inputMX())){
          casadi_warning("Exact Hessian calculation for MX is still experimental");
          
          // Expression for f and g
          MX f = F_mx.outputMX();
          MX g = G_mx.outputMX();
          
          // Lagrange multipliers
          MX lam("lam",g.size1());
      
          // Objective function scaling
          MX sigma("sigma");

          // Inputs of the Lagrangian function
          vector<MX> lfcn_in(parametric_? 4:3);
          lfcn_in[0] = G_mx.inputMX();
          lfcn_in[1] = lam;
          lfcn_in[2] = sigma;
          if (parametric_) lfcn_in[3] = G_mx.inputMX(1);

          // Lagrangian function
          MXFunction lfcn(lfcn_in,sigma*f+ inner_prod(lam,g));
          lfcn.init();
	  log("SX Lagrangian function generated");
          
/*          cout << "countNodes(lfcn.outputMX()) = " << countNodes(lfcn.outputMX()) << endl;*/
      
          bool adjoint_mode = true;
          if(adjoint_mode){
          
            // Gradient of the lagrangian
            MX gL = lfcn.grad();
            log("MX Lagrangian gradient generated");

            MXFunction glfcn(lfcn_in,gL);
            glfcn.init();
            log("MX Lagrangian gradient function initialized");
//           cout << "countNodes(glfcn.outputMX()) = " << countNodes(glfcn.outputMX()) << endl;

            // Get Hessian sparsity
            CRSSparsity H_sp = glfcn.jacSparsity();
            log("MX Lagrangian Hessian sparsity determined");
            
            // Uni-directional coloring (note, the hessian is symmetric)
            CRSSparsity coloring = H_sp.unidirectionalColoring(H_sp);
            log("MX Lagrangian Hessian coloring determined");

            // Number of colors needed is the number of rows
            int nfwd_glfcn = coloring.size1();
            log("MX Lagrangian gradient function number of sensitivity directions determined");

            glfcn.setOption("number_of_fwd_dir",nfwd_glfcn);
            glfcn.updateNumSens();
            log("MX Lagrangian gradient function number of sensitivity directions updated");
            
            // Hessian of the Lagrangian
            H_ = glfcn.jacobian();
          } else {

            // Hessian of the Lagrangian
            H_ = lfcn.hessian();
            
          }
          log("MX Lagrangian Hessian function generated");
          
        } else {
          casadi_assert_message(0, "Automatic calculation of exact Hessian currently only for F and G both SXFunction or MXFunction ");
        }
      } // !F_sx.isNull() && !G_sx.isNull()
    } // G_.isNull()
  } // generate_hessian && H_.isNull()
  if(!H_.isNull() && !H_.isInit()) {
    H_.init();
    log("Hessian function initialized");
  }

  // Create a Jacobian if it does not already exists
  bool generate_jacobian = getOption("generate_jacobian");
  if(generate_jacobian && !G_.isNull() && J_.isNull()){
    log("Generating Jacobian");
    J_ = G_.jacobian();
    
    // Use live variables if SXFunction
    if(!shared_cast<SXFunction>(J_).isNull()){
      J_.setOption("live_variables",true);
    }
    log("Jacobian function generated");
  }
    
  if(!J_.isNull() && !J_.isInit()){
    J_.init();
    log("Jacobian function initialized");
  }

  
  if(!H_.isNull()) {
    if (parametric_) {
      casadi_assert_message(H_.getNumInputs()>=2, "Wrong number of input arguments to H for parametric NLP. Must be at least 2, but got " << G_.getNumInputs());
    } else {
      casadi_assert_message(H_.getNumInputs()>=1, "Wrong number of input arguments to H for non-parametric NLP. Must be at least 1, but got " << G_.getNumInputs() << " instead. Do you perhaps intend to use fixed parameters? Then use the 'parametric' option.");
    }
    casadi_assert_message(H_.getNumOutputs()>=1, "Wrong number of output arguments to H");
    casadi_assert_message(H_.input(0).numel()==n_,"Inconsistent dimensions");
    casadi_assert_message(H_.output().size1()==n_,"Inconsistent dimensions");
    casadi_assert_message(H_.output().size2()==n_,"Inconsistent dimensions");
  }

  if(!J_.isNull()){
    if (parametric_) {
      casadi_assert_message(J_.getNumInputs()==2, "Wrong number of input arguments to J for parametric NLP. Must be at least 2, but got " << G_.getNumInputs());
    } else {
      casadi_assert_message(J_.getNumInputs()==1, "Wrong number of input arguments to J for non-parametric NLP. Must be at least 1, but got " << G_.getNumInputs() << " instead. Do you perhaps intend to use fixed parameters? Then use the 'parametric' option.");
    }
    casadi_assert_message(J_.getNumOutputs()>=1, "Wrong number of output arguments to J");
    casadi_assert_message(J_.input().numel()==n_,"Inconsistent dimensions");
    casadi_assert_message(J_.output().size2()==n_,"Inconsistent dimensions");
  }

  if (parametric_) {
    sp_p = F_->input(1).sparsity();
    
    if (!G_.isNull()) casadi_assert_message(sp_p == G_->input(G_->getNumInputs()-1).sparsity(),"Parametric NLP has inconsistent parameter dimensions. F has got " << sp_p.dimString() << " as dimensions, while G has got " << G_->input(G_->getNumInputs()-1).dimString());
    if (!H_.isNull()) casadi_assert_message(sp_p == H_->input(H_->getNumInputs()-1).sparsity(),"Parametric NLP has inconsistent parameter dimensions. F has got " << sp_p.dimString() << " as dimensions, while H has got " << H_->input(H_->getNumInputs()-1).dimString());
    if (!J_.isNull()) casadi_assert_message(sp_p == J_->input(J_->getNumInputs()-1).sparsity(),"Parametric NLP has inconsistent parameter dimensions. F has got " << sp_p.dimString() << " as dimensions, while J has got " << J_->input(J_->getNumInputs()-1).dimString());
  }
  
  // Infinity
  double inf = numeric_limits<double>::infinity();
  
  // Allocate space for inputs
  input_.resize(NLP_NUM_IN - (parametric_? 0 : 1));
  input(NLP_X_INIT)      = DMatrix(n_,1,0);
  input(NLP_LBX)         = DMatrix(n_,1,-inf);
  input(NLP_UBX)         = DMatrix(n_,1, inf);
  input(NLP_LBG)         = DMatrix(m_,1,-inf);
  input(NLP_UBG)         = DMatrix(m_,1, inf);
  input(NLP_LAMBDA_INIT) = DMatrix(m_,1,0);
  if (parametric_) input(NLP_P) = DMatrix(sp_p,0);
  
  // Allocate space for outputs
  output_.resize(NLP_NUM_OUT);
  output(NLP_X_OPT)      = DMatrix(n_,1,0);
  output(NLP_COST)       = DMatrix(1,1,0);
  output(NLP_LAMBDA_X)   = DMatrix(n_,1,0);
  output(NLP_LAMBDA_G)   = DMatrix(m_,1,0);
  output(NLP_G)          = DMatrix(m_,1,0);
  
  if (hasSetOption("iteration_callback")) {
   callback_ = getOption("iteration_callback");
   if (!callback_.isNull()) {
     if (!callback_.isInit()) callback_.init();
     casadi_assert_message(callback_.getNumOutputs()==1, "Callback function should have one output, a scalar that indicates wether to break. 0 = continue");
     casadi_assert_message(callback_.output(0).size()==1, "Callback function should have one output, a scalar that indicates wether to break. 0 = continue");
     casadi_assert_message(callback_.getNumInputs()==NLP_NUM_OUT, "Callback function should have the output scheme of NLPSolver as input scheme. i.e. " <<NLP_NUM_OUT << " inputs instead of the " << callback_.getNumInputs() << " you provided." );
     for (int i=0;i<NLP_NUM_OUT;i++) {
       casadi_assert_message(callback_.input(i).sparsity()==output(i).sparsity(),
         "Callback function should have the output scheme of NLPSolver as input scheme. " << 
         "Input #" << i << " (" << getSchemeEntryEnumName(SCHEME_NLPOutput,i) <<  " aka '" << getSchemeEntryName(SCHEME_NLPOutput,i) << "') was found to be " << callback_.input(i).dimString() << " instead of expected " << output(i).dimString() << "."
       );
       callback_.input(i).setAll(0);
     }
   }
  }
  
  callback_step_ = getOption("iteration_callback_step");

  // Call the initialization method of the base class
  FXInternal::init();
}