void ImplicitFunctionInternal::init(){
// Initialize the residual function
if(!f_.isInit()) f_.init();
// Allocate inputs
setNumInputs(f_.getNumInputs()-1);
for(int i=0; i<getNumInputs(); ++i){
input(i) = f_.input(i+1);
}
// Allocate outputs
setNumOutputs(f_.getNumOutputs());
output(0) = f_.input(0);
for(int i=1; i<getNumOutputs(); ++i){
output(i) = f_.output(i);
}
// Call the base class initializer
FXInternal::init();
// Number of equations
N_ = output().size();
// Generate Jacobian if not provided
if(J_.isNull()) J_ = f_.jacobian(0,0);
J_.init();
casadi_assert_message(J_.output().size1()==J_.output().size2(),"ImplicitFunctionInternal::init: the jacobian must be square but got " << J_.output().dimString());
casadi_assert_message(!isSingular(J_.output().sparsity()),"ImplicitFunctionInternal::init: singularity - the jacobian is structurally rank-deficient. sprank(J)=" << sprank(J_.output()) << " (in stead of "<< J_.output().size1() << ")");
// Get the linear solver creator function
if(linsol_.isNull() && hasSetOption("linear_solver")){
linearSolverCreator linear_solver_creator = getOption("linear_solver");
// Allocate an NLP solver
linsol_ = linear_solver_creator(CRSSparsity());
// Pass options
if(hasSetOption("linear_solver_options")){
const Dictionary& linear_solver_options = getOption("linear_solver_options");
linsol_.setOption(linear_solver_options);
}
}
// Initialize the linear solver, if provided
if(!linsol_.isNull()){
linsol_.setSparsity(J_.output().sparsity());
linsol_.init();
}
// Allocate memory for directional derivatives
ImplicitFunctionInternal::updateNumSens(false);
}
void QpToImplicit::init() {
// Call the base class initializer
ImplicitFunctionInternal::init();
// Free variable in the NLP
MX u = MX::sym("u", input(iin_).sparsity());
// So that we can pass it on to createParent
std::vector<Sparsity> sps;
for (int i=0; i<getNumInputs(); ++i)
if (i!=iin_) sps.push_back(input(i).sparsity());
// u groups all parameters in an MX
std::vector< MX > inputs;
MX p = createParent(sps, inputs);
// Dummy NLP objective
MX nlp_f = 0;
// NLP constraints
std::vector< MX > args_call(getNumInputs());
args_call[iin_] = u;
for (int i=0, i2=0; i<getNumInputs(); ++i)
if (i!=iin_) args_call[i] = inputs[i2++];
MX nlp_g = f_.call(args_call).at(iout_);
// We're going to use two-argument objective and constraints to allow the use of parameters
MXFunction nlp(nlpIn("x", u, "p", p), nlpOut("f", nlp_f, "g", nlp_g));
// Create an NlpSolver instance
solver_ = NlpSolver(getOption(solvername()), nlp);
if (hasSetOption(optionsname())) solver_.setOption(getOption(optionsname()));
solver_.init();
}
void LrDpleToDple::init() {
// Initialize the base classes
LrDpleInternal::init();
MX As = MX::sym("As", input(LR_DPLE_A).sparsity());
MX Vs = MX::sym("Vs", input(LR_DPLE_V).sparsity());
MX Cs = MX::sym("Cs", input(LR_DPLE_C).sparsity());
MX Hs = MX::sym("Hs", input(LR_DPLE_H).sparsity());
int n_ = A_[0].size1();
// Chop-up the arguments
std::vector<MX> As_ = horzsplit(As, n_);
std::vector<MX> Vs_ = horzsplit(Vs, V_[0].size2());
std::vector<MX> Cs_ = horzsplit(Cs, V_[0].size2());
std::vector<MX> Hss_ = horzsplit(Hs, Hsi_);
std::vector<MX> V_(Vs_.size());
for (int k=0;k<V_.size();++k) {
V_[k] = mul(Cs_[k], mul(Vs_[k], Cs_[k].T()));
}
std::vector<Sparsity> Vsp(Vs_.size());
for (int k=0;k<V_.size();++k) {
Vsp[k] = V_[k].sparsity();
}
// Solver options
Dict options;
if (hasSetOption(optionsname())) {
options = getOption(optionsname());
}
// Create an dplesolver instance
std::map<std::string, std::vector<Sparsity> > tmp;
tmp["a"] = A_;
tmp["v"] = Vsp;
solver_ = DpleSolver("solver", getOption(solvername()), tmp, options);
MX P = solver_(make_map("a", horzcat(As_), "v", horzcat(V_))).at("p");
std::vector<MX> Ps_ = horzsplit(P, n_);
std::vector<MX> HPH(K_);
for (int k=0;k<K_;++k) {
std::vector<MX> hph = horzsplit(Hss_[k], cumsum0(Hs_[k]));
for (int kk=0;kk<hph.size();++kk) {
hph[kk] = mul(hph[kk].T(), mul(Ps_[k], hph[kk]));
}
HPH[k] = diagcat(hph);
}
f_ = MXFunction(name_, lrdpleIn("a", As, "v", Vs, "c", Cs, "h", Hs),
lrdpleOut("y", horzcat(HPH)));
Wrapper<LrDpleToDple>::checkDimensions();
}
void ImplicitFixedStepIntegratorInternal::init() {
// Call the base class init
FixedStepIntegratorInternal::init();
// Get the NLP creator function
std::string implicit_function_name = getOption("implicit_solver");
// Allocate an NLP solver
implicit_solver_ = ImplicitFunction(implicit_function_name, F_, Function(), LinearSolver());
implicit_solver_.setOption("name", string(getOption("name")) + "_implicit_solver");
implicit_solver_.setOption("implicit_input", DAE_Z);
implicit_solver_.setOption("implicit_output", DAE_ALG);
// Pass options
if (hasSetOption("implicit_solver_options")) {
const Dictionary& implicit_solver_options = getOption("implicit_solver_options");
implicit_solver_.setOption(implicit_solver_options);
}
// Initialize the solver
implicit_solver_.init();
// Allocate a root-finding solver for the backward problem
if (nRZ_>0) {
// Get the NLP creator function
std::string backward_implicit_function_name = getOption("implicit_solver");
// Allocate an NLP solver
backward_implicit_solver_ = ImplicitFunction(backward_implicit_function_name,
G_, Function(), LinearSolver());
backward_implicit_solver_.setOption("name",
string(getOption("name")) + "_backward_implicit_solver");
backward_implicit_solver_.setOption("implicit_input", RDAE_RZ);
backward_implicit_solver_.setOption("implicit_output", RDAE_ALG);
// Pass options
if (hasSetOption("implicit_solver_options")) {
const Dictionary& backward_implicit_solver_options = getOption("implicit_solver_options");
backward_implicit_solver_.setOption(backward_implicit_solver_options);
}
// Initialize the solver
backward_implicit_solver_.init();
}
}
void WorhpInternal::init(){
// Call the init method of the base class
NLPSolverInternal::init();
if (hasSetOption("Ares")) {
std::vector<int> ares = getOption("Ares");
std::copy(ares.begin(),ares.begin()+NAres,worhp_p_.Ares);
}
// Read options
passOptions();
// Exact Hessian?
exact_hessian_ = getOption("UserHM");
// Get/generate required functions
gradF();
jacG();
if(exact_hessian_){ // does not appear to work
hessLag();
}
// Update status?
status_[TerminateSuccess]="TerminateSuccess";
status_[OptimalSolution]="OptimalSolution";
status_[SearchDirectionZero]="SearchDirectionZero";
status_[SearchDirectionSmall]="SearchDirectionSmall";
status_[StationaryPointFound]="StationaryPointFound";
status_[AcceptableSolution]="AcceptableSolution";
status_[AcceptablePrevious]="AcceptablePrevious";
status_[FritzJohn]="FritzJohn";
status_[NotDiffable]="NotDiffable";
status_[Unbounded]="Unbounded";
status_[FeasibleSolution]="FeasibleSolution";
status_[LowPassFilterOptimal]="LowPassFilterOptimal";
status_[LowPassFilterAcceptable]="LowPassFilterAcceptable";
status_[TerminateError]="TerminateError";
status_[InitError]="InitError";
status_[DataError]="DataError";
status_[MaxCalls]="MaxCalls";
status_[MaxIter]="MaxIter";
status_[MinimumStepsize]="MinimumStepsize";
status_[QPerror]="QPerror";
status_[ProblemInfeasible]="ProblemInfeasible";
status_[GroupsComposition]="GroupsComposition";
status_[TooBig]="TooBig";
status_[Timeout]="Timeout";
status_[FDError]="FDError";
status_[LocalInfeas]="LocalInfeas";
status_[LicenseError]="LicenseError. Please set the WORHP_LICENSE_FILE environmental variable with the full path to the license file";
status_[TerminatedByUser]="TerminatedByUser";
status_[FunctionErrorF]="FunctionErrorF";
status_[FunctionErrorG]="FunctionErrorG";
status_[FunctionErrorDF]="FunctionErrorDF";
status_[FunctionErrorDG]="FunctionErrorDG";
status_[FunctionErrorHM]="FunctionErrorHM";
}
void ShellCompiler::init() {
// Initialize the base classes
CompilerInternal::init();
// Read options
string compiler = getOption("compiler").toString();
string compiler_setup = getOption("compiler_setup").toString();
vector<string> flags;
if (hasSetOption("flags")) flags = getOption("flags");
// Construct the compiler command
stringstream cmd;
cmd << compiler << " " << compiler_setup;
for (vector<string>::const_iterator i=flags.begin(); i!=flags.end(); ++i) {
cmd << " " << *i;
}
// C/C++ source file
cmd << " " << name_;
// Name of temporary file
#ifdef HAVE_MKSTEMPS
// Preferred solution
char bin_name[] = "tmp_casadi_compiler_shell_XXXXXX.so";
if (mkstemps(bin_name, 3) == -1) {
casadi_error("Failed to create a temporary file name");
}
bin_name_ = bin_name;
#else
// Fallback, may result in deprecation warnings
char* bin_name = tempnam(0, "tmp_casadi_compiler_shell_");
bin_name_ = bin_name;
free(bin_name);
#endif
// Have relative paths start with ./
if (bin_name_.at(0)!='/') {
bin_name_ = "./" + bin_name_;
}
// Temporary file
cmd << " -o " << bin_name_;
// Compile into a shared library
if (system(cmd.str().c_str())) {
casadi_error("Compilation failed. Tried \"" + cmd.str() + "\"");
}
// Load shared library
handle_ = dlopen(bin_name_.c_str(), RTLD_LAZY);
casadi_assert_message(handle_!=0, "CommonExternal: Cannot open function: "
<< bin_name_ << ". error code: "<< dlerror());
// reset error
dlerror();
}
void LpToQp::init() {
// Initialize the base classes
LpSolverInternal::init();
// Create a QpSolver instance
solver_ = QpSolver(getOption(solvername()),
qpStruct("h", Sparsity::sparse(n_, n_),
"a", input(LP_SOLVER_A).sparsity()));
if (hasSetOption(optionsname())) solver_.setOption(getOption(optionsname()));
solver_.init();
}
void Newton::init() {
// Call the base class initializer
ImplicitFunctionInternal::init();
casadi_assert_message(f_.getNumInputs()>0,
"Newton: the supplied f must have at least one input.");
casadi_assert_message(!linsol_.isNull(),
"Newton::init: linear_solver must be supplied");
if (hasSetOption("max_iter"))
max_iter_ = getOption("max_iter");
if (hasSetOption("abstol"))
abstol_ = getOption("abstol");
if (hasSetOption("abstolStep"))
abstolStep_ = getOption("abstolStep");
print_iteration_ = getOption("print_iteration");
}
void QpToQcqp::init() {
// Initialize the base classes
QpSolverInternal::init();
Dict options;
if (hasSetOption(optionsname())) options = getOption(optionsname());
options = OptionsFunctionality::addOptionRecipe(options, "qp");
// Create an QcqpSolver instance
solver_ = QcqpSolver("qcqpsolver", getOption(solvername()),
make_map("h", input(QP_SOLVER_H).sparsity(),
"p", Sparsity(n_, 0),
"a", input(QP_SOLVER_A).sparsity()),
options);
}
void DpleToDle::init() {
DleInternal::init();
// Create an dplesolver instance
std::string dplesolver_name = getOption("dple_solver");
dplesolver_ = DpleSolver(dplesolver_name, dpleStruct("a",
std::vector<Sparsity>(1,A_), "v",std::vector<Sparsity>(1,V_),"c",std::vector<Sparsity>(1,C_),"h",std::vector<Sparsity>(1,H_))
);
if (hasSetOption("dple")) {
dplesolver_.setOption(getOption("dple"));
}
// Initialize the NLP solver
dplesolver_.init();
}
void QCQPQPInternal::init() {
QpSolverInternal::init();
// Create an qcqpsolver instance
std::string qcqpsolver_name = getOption("qcqp_solver");
qcqpsolver_ = QcqpSolver(qcqpsolver_name,
qcqpStruct("h", input(QP_SOLVER_H).sparsity(),
"p", Sparsity::sparse(n_, 0),
"a", input(QP_SOLVER_A).sparsity()));
qcqpsolver_.setQPOptions();
if (hasSetOption("qcqp_solver_options")) {
qcqpsolver_.setOption(getOption("qcqp_solver_options"));
}
// Initialize the NLP solver
qcqpsolver_.init();
}
void QcqpToSocp::init() {
// Initialize the base classes
QcqpSolverInternal::init();
// Collection of sparsities that will make up SOCP_SOLVER_G
std::vector<Sparsity> socp_g;
// Allocate Cholesky solvers
cholesky_.push_back(LinearSolver("csparsecholesky", st_[QCQP_STRUCT_H]));
for (int i=0;i<nq_;++i) {
cholesky_.push_back(
LinearSolver("csparsecholesky",
DMatrix(st_[QCQP_STRUCT_P])(range(i*n_, (i+1)*n_), ALL).sparsity()));
}
for (int i=0;i<nq_+1;++i) {
// Initialize Cholesky solve
cholesky_[i].init();
// Harvest Cholsesky sparsity patterns
// Note that we add extra scalar to make room for the epigraph-reformulation variable
socp_g.push_back(blkdiag(
cholesky_[i].getFactorizationSparsity(false), Sparsity::dense(1, 1)));
}
// Create an SocpSolver instance
solver_ = SocpSolver(getOption(solvername()),
socpStruct("g", horzcat(socp_g),
"a", horzcat(input(QCQP_SOLVER_A).sparsity(),
Sparsity::sparse(nc_, 1))));
//solver_.setQCQPOptions();
if (hasSetOption(optionsname())) solver_.setOption(getOption(optionsname()));
std::vector<int> ni(nq_+1);
for (int i=0;i<nq_+1;++i) {
ni[i] = n_+1;
}
solver_.setOption("ni", ni);
// Initialize the SocpSolver
solver_.init();
}
void StabilizedQpToQp::init() {
// Initialize the base classes
StabilizedQpSolverInternal::init();
// Form augmented QP
Sparsity H_sparsity_qp = diagcat(st_[QP_STRUCT_H], Sparsity::diag(nc_));
Sparsity A_sparsity_qp = horzcat(st_[QP_STRUCT_A], Sparsity::diag(nc_));
std::string qp_solver_name = getOption("qp_solver");
qp_solver_ = QpSolver(qp_solver_name,
qpStruct("h", H_sparsity_qp, "a", A_sparsity_qp));
// Pass options if provided
if (hasSetOption("qp_solver_options")) {
Dictionary qp_solver_options = getOption("qp_solver_options");
qp_solver_.setOption(qp_solver_options);
}
// Initialize the QP solver
qp_solver_.init();
}
bool IpoptInternal::get_starting_point(int n, bool init_x, double* x,
bool init_z, double* z_L, double* z_U,
int m, bool init_lambda,
double* lambda)
{
try {
bool warmstart = hasSetOption("warm_start_init_point") && getOption("warm_start_init_point")=="yes";
//casadi_assert_warning(init_x,"Not initializing x");
if (warmstart) {
//casadi_assert_warning(init_lambda,"Not initializing lambda");
//casadi_assert_warning(init_z,"Not initializing z");
}
if(init_x){
input(NLP_SOLVER_X0).getArray(x,n);
}
if (init_z) {
// Get dual solution (simple bounds)
vector<double>& lambda_x = input(NLP_SOLVER_LAM_X0).data();
for(int i=0; i<lambda_x.size(); ++i){
z_L[i] = max(0.,-lambda_x[i]);
z_U[i] = max(0., lambda_x[i]);
}
}
if (init_lambda){
input(NLP_SOLVER_LAM_G0).getArray(lambda,m);
}
return true;
} catch (exception& ex){
cerr << "get_starting_point failed: " << ex.what() << endl;
return false;
}
}
void QpToImplicit::init() {
// Call the base class initializer
ImplicitFunctionInternal::init();
// Free variable in the NLP
MX u = MX::sym("u", input(iin_).sparsity());
// So that we can pass it on to createParent
std::vector<MX> inputs;
for (int i=0; i<nIn(); ++i) {
if (i!=iin_) {
stringstream ss;
ss << "p" << i;
inputs.push_back(MX::sym(ss.str(), input(i).sparsity()));
}
}
MX p = veccat(inputs);
// Dummy NLP objective
MX nlp_f = 0;
// NLP constraints
std::vector< MX > args_call(nIn());
args_call[iin_] = u;
for (int i=0, i2=0; i<nIn(); ++i)
if (i!=iin_) args_call[i] = inputs[i2++];
MX nlp_g = f_(args_call).at(iout_);
// We're going to use two-argument objective and constraints to allow the use of parameters
MXFunction nlp("nlp", nlpIn("x", u, "p", p), nlpOut("f", nlp_f, "g", nlp_g));
Dict options;
if (hasSetOption(optionsname())) options = getOption(optionsname());
// Create an NlpSolver instance
solver_ = NlpSolver("nlpsolver", getOption(solvername()), nlp, options);
}
void SdqpToSdp::init() {
// Initialize the base classes
SdqpSolverInternal::init();
cholesky_ = LinearSolver("cholesky", "csparsecholesky", st_[SDQP_STRUCT_H]);
MX g_socp = MX::sym("x", cholesky_.getFactorizationSparsity(true));
MX h_socp = MX::sym("h", n_);
MX f_socp = sqrt(inner_prod(h_socp, h_socp));
MX en_socp = 0.5/f_socp;
MX f_sdqp = MX::sym("f", input(SDQP_SOLVER_F).sparsity());
MX g_sdqp = MX::sym("g", input(SDQP_SOLVER_G).sparsity());
std::vector<MX> fi(n_+1);
MX znp = MX(n_+1, n_+1);
for (int k=0;k<n_;++k) {
MX gk = vertcat(g_socp(ALL, k), MX(1, 1));
MX fk = -blockcat(znp, gk, gk.T(), MX(1, 1));
// TODO(Joel): replace with ALL
fi.push_back(diagcat(f_sdqp(ALL, Slice(f_sdqp.size1()*k, f_sdqp.size1()*(k+1))), fk));
}
MX fin = en_socp*DMatrix::eye(n_+2);
fin(n_, n_+1) = en_socp;
fin(n_+1, n_) = en_socp;
fi.push_back(diagcat(DMatrix(f_sdqp.size1(), f_sdqp.size1()), -fin));
MX h0 = vertcat(h_socp, DMatrix(1, 1));
MX g = blockcat(f_socp*DMatrix::eye(n_+1), h0, h0.T(), f_socp);
g = diagcat(g_sdqp, g);
Dict opts;
opts["input_scheme"] = IOScheme("g_socp", "h_socp", "f_sdqp", "g_sdqp");
opts["output_scheme"] = IOScheme("f", "g");
mapping_ = MXFunction("mapping", make_vector(g_socp, h_socp, f_sdqp, g_sdqp),
make_vector(horzcat(fi), g), opts);
Dict options;
if (hasSetOption(optionsname())) options = getOption(optionsname());
// Create an SdpSolver instance
solver_ = SdpSolver("sdpsolver", getOption(solvername()),
make_map("g", mapping_.output("g").sparsity(),
"f", mapping_.output("f").sparsity(),
"a", horzcat(input(SDQP_SOLVER_A).sparsity(),
Sparsity(nc_, 1))),
options);
solver_.input(SDP_SOLVER_C).at(n_)=1;
// Output arguments
obuf_.resize(SDQP_SOLVER_NUM_OUT);
output(SDQP_SOLVER_X) = DMatrix::zeros(n_, 1);
std::vector<int> r = range(input(SDQP_SOLVER_G).size1());
output(SDQP_SOLVER_P) = solver_.output(SDP_SOLVER_P).isempty() ? DMatrix() :
solver_.output(SDP_SOLVER_P)(r, r);
output(SDQP_SOLVER_DUAL) = solver_.output(SDP_SOLVER_DUAL).isempty() ? DMatrix() :
solver_.output(SDP_SOLVER_DUAL)(r, r);
output(SDQP_SOLVER_COST) = 0.0;
output(SDQP_SOLVER_DUAL_COST) = 0.0;
output(SDQP_SOLVER_LAM_X) = DMatrix::zeros(n_, 1);
output(SDQP_SOLVER_LAM_A) = DMatrix::zeros(nc_, 1);
}
void DirectCollocationInternal::init(){
// Initialize the base classes
OCPSolverInternal::init();
// Free parameters currently not supported
casadi_assert_message(np_==0, "Not implemented");
// Legendre collocation points
double legendre_points[][6] = {
{0},
{0,0.500000},
{0,0.211325,0.788675},
{0,0.112702,0.500000,0.887298},
{0,0.069432,0.330009,0.669991,0.930568},
{0,0.046910,0.230765,0.500000,0.769235,0.953090}};
// Radau collocation points
double radau_points[][6] = {
{0},
{0,1.000000},
{0,0.333333,1.000000},
{0,0.155051,0.644949,1.000000},
{0,0.088588,0.409467,0.787659,1.000000},
{0,0.057104,0.276843,0.583590,0.860240,1.000000}};
// Read options
bool use_radau;
if(getOption("collocation_scheme")=="radau"){
use_radau = true;
} else if(getOption("collocation_scheme")=="legendre"){
use_radau = false;
}
// Interpolation order
deg_ = getOption("interpolation_order");
// All collocation time points
double* tau_root = use_radau ? radau_points[deg_] : legendre_points[deg_];
// Size of the finite elements
double h = tf_/nk_;
// Coefficients of the collocation equation
vector<vector<MX> > C(deg_+1,vector<MX>(deg_+1));
// Coefficients of the collocation equation as DMatrix
DMatrix C_num = DMatrix(deg_+1,deg_+1,0);
// Coefficients of the continuity equation
vector<MX> D(deg_+1);
// Coefficients of the collocation equation as DMatrix
DMatrix D_num = DMatrix(deg_+1,1,0);
// Collocation point
SXMatrix tau = ssym("tau");
// For all collocation points
for(int j=0; j<deg_+1; ++j){
// Construct Lagrange polynomials to get the polynomial basis at the collocation point
SXMatrix L = 1;
for(int j2=0; j2<deg_+1; ++j2){
if(j2 != j){
L *= (tau-tau_root[j2])/(tau_root[j]-tau_root[j2]);
}
}
SXFunction lfcn(tau,L);
lfcn.init();
// Evaluate the polynomial at the final time to get the coefficients of the continuity equation
lfcn.setInput(1.0);
lfcn.evaluate();
D[j] = lfcn.output();
D_num(j) = lfcn.output();
// Evaluate the time derivative of the polynomial at all collocation points to get the coefficients of the continuity equation
for(int j2=0; j2<deg_+1; ++j2){
lfcn.setInput(tau_root[j2]);
lfcn.setFwdSeed(1.0);
lfcn.evaluate(1,0);
C[j][j2] = lfcn.fwdSens();
C_num(j,j2) = lfcn.fwdSens();
}
}
C_num(std::vector<int>(1,0),ALL) = 0;
C_num(0,0) = 1;
// All collocation time points
vector<vector<double> > T(nk_);
for(int k=0; k<nk_; ++k){
T[k].resize(deg_+1);
for(int j=0; j<=deg_; ++j){
T[k][j] = h*(k + tau_root[j]);
}
}
// Total number of variables
int nlp_nx = 0;
nlp_nx += nk_*(deg_+1)*nx_; // Collocated states
nlp_nx += nk_*nu_; // Parametrized controls
nlp_nx += nx_; // Final state
// NLP variable vector
MX nlp_x = msym("x",nlp_nx);
int offset = 0;
// Get collocated states and parametrized control
vector<vector<MX> > X(nk_+1);
vector<MX> U(nk_);
for(int k=0; k<nk_; ++k){
// Collocated states
X[k].resize(deg_+1);
for(int j=0; j<=deg_; ++j){
// Get the expression for the state vector
X[k][j] = nlp_x[Slice(offset,offset+nx_)];
offset += nx_;
}
// Parametrized controls
U[k] = nlp_x[Slice(offset,offset+nu_)];
offset += nu_;
}
// State at end time
X[nk_].resize(1);
X[nk_][0] = nlp_x[Slice(offset,offset+nx_)];
offset += nx_;
casadi_assert(offset==nlp_nx);
// Constraint function for the NLP
vector<MX> nlp_g;
// Objective function
MX nlp_j = 0;
// For all finite elements
for(int k=0; k<nk_; ++k){
// For all collocation points
for(int j=1; j<=deg_; ++j){
// Get an expression for the state derivative at the collocation point
MX xp_jk = 0;
for(int r=0; r<=deg_; ++r){
xp_jk += C[r][j]*X[k][r];
}
// Add collocation equations to the NLP
MX fk = ffcn_.call(daeIn("x",X[k][j],"p",U[k]))[DAE_ODE];
nlp_g.push_back(h*fk - xp_jk);
}
// Get an expression for the state at the end of the finite element
MX xf_k = 0;
for(int r=0; r<=deg_; ++r){
xf_k += D[r]*X[k][r];
}
// Add continuity equation to NLP
nlp_g.push_back(X[k+1][0] - xf_k);
// Add path constraints
if(nh_>0){
MX pk = cfcn_.call(daeIn("x",X[k+1][0],"p",U[k])).at(0);
nlp_g.push_back(pk);
}
// Add integral objective function term
// [Jk] = lfcn.call([X[k+1,0], U[k]])
// nlp_j += Jk
}
// Add end cost
MX Jk = mfcn_.call(mayerIn("x",X[nk_][0])).at(0);
nlp_j += Jk;
// Objective function of the NLP
F_ = MXFunction(nlp_x, nlp_j);
// Nonlinear constraint function
G_ = MXFunction(nlp_x, vertcat(nlp_g));
// Get the NLP creator function
NLPSolverCreator nlp_solver_creator = getOption("nlp_solver");
// Allocate an NLP solver
nlp_solver_ = nlp_solver_creator(F_,G_,FX(),FX());
// Pass options
if(hasSetOption("nlp_solver_options")){
const Dictionary& nlp_solver_options = getOption("nlp_solver_options");
nlp_solver_.setOption(nlp_solver_options);
}
// Initialize the solver
nlp_solver_.init();
}
void WorhpInternal::passOptions() {
for (int i=0;i<WorhpGetParamCount();++i) {
WorhpType type = WorhpGetParamType(i+1);
const char* name = WorhpGetParamName(i+1);
if (strcmp(name,"Ares")==0) continue;
switch(type) {
case WORHP_BOOL_T:
if (hasSetOption(name)) WorhpSetBoolParam(&worhp_p_, name, getOption(name));
break;
case WORHP_DOUBLE_T:
if (hasSetOption(name)) WorhpSetDoubleParam(&worhp_p_, name, getOption(name));
break;
case WORHP_INT_T:
if (hasSetOption(name)) WorhpSetIntParam(&worhp_p_, name, getOption(name));
break;
default:
break;// do nothing
}
}
if (hasSetOption("qp_ipBarrier")) worhp_p_.qp.ipBarrier = getOption("qp_ipBarrier");
if (hasSetOption("qp_ipComTol")) worhp_p_.qp.ipComTol = getOption("qp_ipComTol");
if (hasSetOption("qp_ipFracBound")) worhp_p_.qp.ipFracBound = getOption("qp_ipFracBound");
if (hasSetOption("qp_ipLsMethod")) worhp_p_.qp.ipLsMethod = getOptionEnumValue("qp_ipLsMethod");
if (hasSetOption("qp_ipMinAlpha")) worhp_p_.qp.ipMinAlpha = getOption("qp_ipMinAlpha");
if (hasSetOption("qp_ipTryRelax")) worhp_p_.qp.ipTryRelax = getOption("qp_ipTryRelax");
if (hasSetOption("qp_ipRelaxDiv")) worhp_p_.qp.ipRelaxDiv = getOption("qp_ipRelaxDiv");
if (hasSetOption("qp_ipRelaxMult")) worhp_p_.qp.ipRelaxMult = getOption("qp_ipRelaxMult");
if (hasSetOption("qp_ipRelaxMax")) worhp_p_.qp.ipRelaxMax = getOption("qp_ipRelaxMax");
if (hasSetOption("qp_ipRelaxMin")) worhp_p_.qp.ipRelaxMin = getOption("qp_ipRelaxMin");
if (hasSetOption("qp_ipResTol")) worhp_p_.qp.ipResTol = getOption("qp_ipResTol");
if (hasSetOption("qp_lsItMaxIter")) worhp_p_.qp.lsItMaxIter = getOption("qp_lsItMaxIter");
if (hasSetOption("qp_lsItMethod")) worhp_p_.qp.lsItMethod = getOptionEnumValue("qp_lsItMethod");
if (hasSetOption("qp_lsItPrecondMethod")) worhp_p_.qp.lsItPrecondMethod = getOptionEnumValue("qp_lsItPrecondMethod");
if (hasSetOption("qp_lsRefineMaxIter")) worhp_p_.qp.lsRefineMaxIter = getOption("qp_lsRefineMaxIter");
if (hasSetOption("qp_lsScale")) worhp_p_.qp.lsScale = getOption("qp_lsScale");
if (hasSetOption("qp_lsTrySimple")) worhp_p_.qp.lsTrySimple = getOption("qp_lsTrySimple");
if (hasSetOption("qp_lsTol")) worhp_p_.qp.lsTol = getOption("qp_lsTol");
if (hasSetOption("qp_maxIter")) worhp_p_.qp.maxIter = getOption("qp_maxIter");
if (hasSetOption("qp_method")) worhp_p_.qp.method = getOptionEnumValue("qp_method");
if (hasSetOption("qp_nsnBeta")) worhp_p_.qp.nsnBeta = getOption("qp_nsnBeta");
if (hasSetOption("qp_nsnGradStep")) worhp_p_.qp.nsnGradStep = getOption("qp_nsnGradStep");
if (hasSetOption("qp_nsnKKT")) worhp_p_.qp.nsnKKT = getOption("qp_nsnKKT");
if (hasSetOption("qp_nsnLsMethod")) worhp_p_.qp.nsnLsMethod = getOptionEnumValue("qp_nsnLsMethod");
if (hasSetOption("qp_nsnMinAlpha")) worhp_p_.qp.nsnMinAlpha = getOption("qp_nsnMinAlpha");
if (hasSetOption("qp_nsnSigma")) worhp_p_.qp.nsnSigma = getOption("qp_nsnSigma");
if (hasSetOption("qp_printLevel")) worhp_p_.qp.printLevel = getOptionEnumValue("qp_printLevel");
if (hasSetOption("qp_scaleIntern")) worhp_p_.qp.scaleIntern = getOption("qp_scaleIntern");
if (hasSetOption("qp_strict")) worhp_p_.qp.strict = getOption("qp_strict");
// Mark the parameters as set
worhp_p_.initialised = true;
}
void SDPSDQPInternal::init() {
// Initialize the base classes
SdqpSolverInternal::init();
cholesky_ = LinearSolver("csparsecholesky", st_[SDQP_STRUCT_H]);
cholesky_.init();
MX g_socp = MX::sym("x", cholesky_.getFactorizationSparsity(true));
MX h_socp = MX::sym("h", n_);
MX f_socp = sqrt(inner_prod(h_socp, h_socp));
MX en_socp = 0.5/f_socp;
MX f_sdqp = MX::sym("f", input(SDQP_SOLVER_F).sparsity());
MX g_sdqp = MX::sym("g", input(SDQP_SOLVER_G).sparsity());
std::vector<MX> fi(n_+1);
MX znp = MX::sparse(n_+1, n_+1);
for (int k=0;k<n_;++k) {
MX gk = vertcat(g_socp(ALL, k), DMatrix::sparse(1, 1));
MX fk = -blockcat(znp, gk, gk.T(), DMatrix::sparse(1, 1));
// TODO(Joel): replace with ALL
fi.push_back(blkdiag(f_sdqp(ALL, Slice(f_sdqp.size1()*k, f_sdqp.size1()*(k+1))), fk));
}
MX fin = en_socp*DMatrix::eye(n_+2);
fin(n_, n_+1) = en_socp;
fin(n_+1, n_) = en_socp;
fi.push_back(blkdiag(DMatrix::sparse(f_sdqp.size1(), f_sdqp.size1()), -fin));
MX h0 = vertcat(h_socp, DMatrix::sparse(1, 1));
MX g = blockcat(f_socp*DMatrix::eye(n_+1), h0, h0.T(), f_socp);
g = blkdiag(g_sdqp, g);
IOScheme mappingIn("g_socp", "h_socp", "f_sdqp", "g_sdqp");
IOScheme mappingOut("f", "g");
mapping_ = MXFunction(mappingIn("g_socp", g_socp, "h_socp", h_socp,
"f_sdqp", f_sdqp, "g_sdqp", g_sdqp),
mappingOut("f", horzcat(fi), "g", g));
mapping_.init();
// Create an sdpsolver instance
std::string sdpsolver_name = getOption("sdp_solver");
sdpsolver_ = SdpSolver(sdpsolver_name,
sdpStruct("g", mapping_.output("g").sparsity(),
"f", mapping_.output("f").sparsity(),
"a", horzcat(input(SDQP_SOLVER_A).sparsity(),
Sparsity::sparse(nc_, 1))));
if (hasSetOption("sdp_solver_options")) {
sdpsolver_.setOption(getOption("sdp_solver_options"));
}
// Initialize the SDP solver
sdpsolver_.init();
sdpsolver_.input(SDP_SOLVER_C).at(n_)=1;
// Output arguments
setNumOutputs(SDQP_SOLVER_NUM_OUT);
output(SDQP_SOLVER_X) = DMatrix::zeros(n_, 1);
std::vector<int> r = range(input(SDQP_SOLVER_G).size1());
output(SDQP_SOLVER_P) = sdpsolver_.output(SDP_SOLVER_P).isEmpty() ? DMatrix() :
sdpsolver_.output(SDP_SOLVER_P)(r, r);
output(SDQP_SOLVER_DUAL) = sdpsolver_.output(SDP_SOLVER_DUAL).isEmpty() ? DMatrix() :
sdpsolver_.output(SDP_SOLVER_DUAL)(r, r);
output(SDQP_SOLVER_COST) = 0.0;
output(SDQP_SOLVER_DUAL_COST) = 0.0;
output(SDQP_SOLVER_LAM_X) = DMatrix::zeros(n_, 1);
output(SDQP_SOLVER_LAM_A) = DMatrix::zeros(nc_, 1);
}
void DirectCollocationInternal::init(){
// Initialize the base classes
OCPSolverInternal::init();
// Free parameters currently not supported
casadi_assert_message(np_==0, "Not implemented");
// Interpolation order
deg_ = getOption("interpolation_order");
// All collocation time points
std::vector<double> tau_root = collocationPoints(deg_,getOption("collocation_scheme"));
// Size of the finite elements
double h = tf_/nk_;
// Coefficients of the collocation equation
vector<vector<MX> > C(deg_+1,vector<MX>(deg_+1));
// Coefficients of the collocation equation as DMatrix
DMatrix C_num = DMatrix::zeros(deg_+1,deg_+1);
// Coefficients of the continuity equation
vector<MX> D(deg_+1);
// Coefficients of the collocation equation as DMatrix
DMatrix D_num = DMatrix::zeros(deg_+1);
// Collocation point
SX tau = SX::sym("tau");
// For all collocation points
for(int j=0; j<deg_+1; ++j){
// Construct Lagrange polynomials to get the polynomial basis at the collocation point
SX L = 1;
for(int j2=0; j2<deg_+1; ++j2){
if(j2 != j){
L *= (tau-tau_root[j2])/(tau_root[j]-tau_root[j2]);
}
}
SXFunction lfcn(tau,L);
lfcn.init();
// Evaluate the polynomial at the final time to get the coefficients of the continuity equation
lfcn.setInput(1.0);
lfcn.evaluate();
D[j] = lfcn.output();
D_num(j) = lfcn.output();
// Evaluate the time derivative of the polynomial at all collocation points to get the coefficients of the continuity equation
Function tfcn = lfcn.tangent();
tfcn.init();
for(int j2=0; j2<deg_+1; ++j2){
tfcn.setInput(tau_root[j2]);
tfcn.evaluate();
C[j][j2] = tfcn.output();
C_num(j,j2) = tfcn.output();
}
}
C_num(std::vector<int>(1,0),ALL) = 0;
C_num(0,0) = 1;
// All collocation time points
vector<vector<double> > T(nk_);
for(int k=0; k<nk_; ++k){
T[k].resize(deg_+1);
for(int j=0; j<=deg_; ++j){
T[k][j] = h*(k + tau_root[j]);
}
}
// Total number of variables
int nlp_nx = 0;
nlp_nx += nk_*(deg_+1)*nx_; // Collocated states
nlp_nx += nk_*nu_; // Parametrized controls
nlp_nx += nx_; // Final state
// NLP variable vector
MX nlp_x = MX::sym("x",nlp_nx);
int offset = 0;
// Get collocated states and parametrized control
vector<vector<MX> > X(nk_+1);
vector<MX> U(nk_);
for(int k=0; k<nk_; ++k){
// Collocated states
X[k].resize(deg_+1);
for(int j=0; j<=deg_; ++j){
// Get the expression for the state vector
X[k][j] = nlp_x[Slice(offset,offset+nx_)];
offset += nx_;
}
// Parametrized controls
U[k] = nlp_x[Slice(offset,offset+nu_)];
offset += nu_;
}
// State at end time
X[nk_].resize(1);
X[nk_][0] = nlp_x[Slice(offset,offset+nx_)];
offset += nx_;
casadi_assert(offset==nlp_nx);
// Constraint function for the NLP
vector<MX> nlp_g;
// Objective function
MX nlp_j = 0;
// For all finite elements
for(int k=0; k<nk_; ++k){
// For all collocation points
for(int j=1; j<=deg_; ++j){
// Get an expression for the state derivative at the collocation point
MX xp_jk = 0;
for(int r=0; r<=deg_; ++r){
xp_jk += C[r][j]*X[k][r];
}
// Add collocation equations to the NLP
MX fk = ffcn_.call(daeIn("x",X[k][j],"p",U[k]))[DAE_ODE];
nlp_g.push_back(h*fk - xp_jk);
}
// Get an expression for the state at the end of the finite element
MX xf_k = 0;
for(int r=0; r<=deg_; ++r){
xf_k += D[r]*X[k][r];
}
// Add continuity equation to NLP
nlp_g.push_back(X[k+1][0] - xf_k);
// Add path constraints
if(nh_>0){
MX pk = cfcn_.call(daeIn("x",X[k+1][0],"p",U[k])).at(0);
nlp_g.push_back(pk);
}
// Add integral objective function term
// [Jk] = lfcn.call([X[k+1,0], U[k]])
// nlp_j += Jk
}
// Add end cost
MX Jk = mfcn_.call(mayerIn("x",X[nk_][0])).at(0);
nlp_j += Jk;
// Objective function of the NLP
nlp_ = MXFunction(nlpIn("x",nlp_x), nlpOut("f",nlp_j,"g",vertcat(nlp_g)));
// Get the NLP creator function
NLPSolverCreator nlp_solver_creator = getOption("nlp_solver");
// Allocate an NLP solver
nlp_solver_ = nlp_solver_creator(nlp_);
// Pass options
if(hasSetOption("nlp_solver_options")){
const Dictionary& nlp_solver_options = getOption("nlp_solver_options");
nlp_solver_.setOption(nlp_solver_options);
}
// Initialize the solver
nlp_solver_.init();
}
void Sqpmethod::init() {
// Call the init method of the base class
NlpSolverInternal::init();
// Read options
max_iter_ = getOption("max_iter");
max_iter_ls_ = getOption("max_iter_ls");
c1_ = getOption("c1");
beta_ = getOption("beta");
merit_memsize_ = getOption("merit_memory");
lbfgs_memory_ = getOption("lbfgs_memory");
tol_pr_ = getOption("tol_pr");
tol_du_ = getOption("tol_du");
regularize_ = getOption("regularize");
exact_hessian_ = getOption("hessian_approximation")=="exact";
min_step_size_ = getOption("min_step_size");
// Get/generate required functions
gradF();
jacG();
if (exact_hessian_) {
hessLag();
}
// Allocate a QP solver
Sparsity H_sparsity = exact_hessian_ ? hessLag().output().sparsity()
: Sparsity::dense(nx_, nx_);
H_sparsity = H_sparsity + Sparsity::diag(nx_);
Sparsity A_sparsity = jacG().isNull() ? Sparsity(0, nx_)
: jacG().output().sparsity();
// QP solver options
Dict qp_solver_options;
if (hasSetOption("qp_solver_options")) {
qp_solver_options = getOption("qp_solver_options");
}
// Allocate a QP solver
qp_solver_ = QpSolver("qp_solver", getOption("qp_solver"),
make_map("h", H_sparsity, "a", A_sparsity),
qp_solver_options);
// Lagrange multipliers of the NLP
mu_.resize(ng_);
mu_x_.resize(nx_);
// Lagrange gradient in the next iterate
gLag_.resize(nx_);
gLag_old_.resize(nx_);
// Current linearization point
x_.resize(nx_);
x_cand_.resize(nx_);
x_old_.resize(nx_);
// Constraint function value
gk_.resize(ng_);
gk_cand_.resize(ng_);
// Hessian approximation
Bk_ = DMatrix::zeros(H_sparsity);
// Jacobian
Jk_ = DMatrix::zeros(A_sparsity);
// Bounds of the QP
qp_LBA_.resize(ng_);
qp_UBA_.resize(ng_);
qp_LBX_.resize(nx_);
qp_UBX_.resize(nx_);
// QP solution
dx_.resize(nx_);
qp_DUAL_X_.resize(nx_);
qp_DUAL_A_.resize(ng_);
// Gradient of the objective
gf_.resize(nx_);
// Create Hessian update function
if (!exact_hessian_) {
// Create expressions corresponding to Bk, x, x_old, gLag and gLag_old
SX Bk = SX::sym("Bk", H_sparsity);
SX x = SX::sym("x", input(NLP_SOLVER_X0).sparsity());
SX x_old = SX::sym("x", x.sparsity());
SX gLag = SX::sym("gLag", x.sparsity());
SX gLag_old = SX::sym("gLag_old", x.sparsity());
SX sk = x - x_old;
SX yk = gLag - gLag_old;
SX qk = mul(Bk, sk);
// Calculating theta
SX skBksk = inner_prod(sk, qk);
SX omega = if_else(inner_prod(yk, sk) < 0.2 * inner_prod(sk, qk),
0.8 * skBksk / (skBksk - inner_prod(sk, yk)),
1);
yk = omega * yk + (1 - omega) * qk;
SX theta = 1. / inner_prod(sk, yk);
SX phi = 1. / inner_prod(qk, sk);
SX Bk_new = Bk + theta * mul(yk, yk.T()) - phi * mul(qk, qk.T());
// Inputs of the BFGS update function
vector<SX> bfgs_in(BFGS_NUM_IN);
bfgs_in[BFGS_BK] = Bk;
bfgs_in[BFGS_X] = x;
bfgs_in[BFGS_X_OLD] = x_old;
bfgs_in[BFGS_GLAG] = gLag;
bfgs_in[BFGS_GLAG_OLD] = gLag_old;
bfgs_ = SXFunction("bfgs", bfgs_in, make_vector(Bk_new));
// Initial Hessian approximation
B_init_ = DMatrix::eye(nx_);
}
// Header
if (static_cast<bool>(getOption("print_header"))) {
userOut()
<< "-------------------------------------------" << endl
<< "This is casadi::SQPMethod." << endl;
if (exact_hessian_) {
userOut() << "Using exact Hessian" << endl;
} else {
userOut() << "Using limited memory BFGS Hessian approximation" << endl;
}
userOut()
<< endl
<< "Number of variables: " << setw(9) << nx_ << endl
<< "Number of constraints: " << setw(9) << ng_ << endl
<< "Number of nonzeros in constraint Jacobian: " << setw(9) << A_sparsity.nnz() << endl
<< "Number of nonzeros in Lagrangian Hessian: " << setw(9) << H_sparsity.nnz() << endl
<< endl;
}
}
void AcadoOCPInternal::evaluate(int nfdir, int nadir){
// Initial constraint function
if(!rfcn_.f_.isNull()){
const Matrix<double>& lbr = input(ACADO_LBR);
ACADO::Vector lb(lbr.size(),&lbr.front());
const Matrix<double>& ubr = input(ACADO_UBR);
ACADO::Vector ub(ubr.size(),&ubr.front());
ocp_->subjectTo( ACADO::AT_START, lb <= (*rfcn_.fcn_)(*arg_) <= ub);
}
// Path constraint function
if(!cfcn_.f_.isNull()){
const Matrix<double>& lbc = input(ACADO_LBC);
ACADO::Vector lb(lbc.size(),&lbc.front());
const Matrix<double>& ubc = input(ACADO_UBC);
ACADO::Vector ub(ubc.size(),&ubc.front());
ocp_->subjectTo( lb <= (*cfcn_.fcn_)(*arg_) <= ub );
}
// State bounds
Matrix<double> &lbx = input(ACADO_LBX);
Matrix<double> &ubx = input(ACADO_UBX);
for(int i=0; i<nxd_; ++i)
ocp_->subjectTo( lbx.at(i) <= xd_[i] <= ubx.at(i) );
for(int i=nxd_; i<nx_; ++i)
ocp_->subjectTo( lbx.at(i) <= xa_[i-nxd_] <= ubx.at(i) );
// Pass bounds on state at initial time
Matrix<double> &lbx0 = input(ACADO_LBX0);
Matrix<double> &ubx0 = input(ACADO_UBX0);
for(int i=0; i<nxd_; ++i)
ocp_->subjectTo( ACADO::AT_START, lbx0.at(i) <= xd_[i] <= ubx0.at(i) );
for(int i=nxd_; i<nx_; ++i)
ocp_->subjectTo( ACADO::AT_START, lbx0.at(i) <= xa_[i-nxd_] <= ubx0.at(i) );
// ocp_->subjectTo( AT_END , xd_[1] == 0.0 );
// ocp_->subjectTo( AT_END , xd_[2] == 0.0 );
// Control bounds
Matrix<double> &lbu = input(ACADO_LBU);
Matrix<double> &ubu = input(ACADO_UBU);
for(int i=0; i<nu_; ++i)
ocp_->subjectTo( lbu.at(i) <= u_[i] <= ubu.at(i) );
// Parameter bounds
Matrix<double> &lbp = input(ACADO_LBP);
Matrix<double> &ubp = input(ACADO_UBP);
for(int i=0; i<np_; ++i)
ocp_->subjectTo( lbp.at(i) <= p_[i] <= ubp.at(i) );
// Periodic boundary condition
if(hasSetOption("periodic_bounds")){
const vector<int>& periodic = getOption("periodic_bounds");
if(periodic.size()!=nx_) throw CasadiException("wrong dimension for periodic_bounds");
for(int i=0; i<nxd_; ++i)
if(periodic[i])
ocp_->subjectTo( 0.0, xd_[i], -xd_[i], 0.0);
for(int i=nxd_; i<nx_; ++i)
if(periodic[i])
ocp_->subjectTo( 0.0, xa_[i-nxd_], -xa_[i-nxd_], 0.0);
}
algorithm_ = new ACADO::OptimizationAlgorithm(*ocp_);
// set print level
ACADO::PrintLevel printlevel;
if(getOption("print_level")=="none") printlevel = ACADO::NONE;
else if(getOption("print_level")=="low") printlevel = ACADO::LOW;
else if(getOption("print_level")=="medium") printlevel = ACADO::MEDIUM;
else if(getOption("print_level")=="high") printlevel = ACADO::HIGH;
else if(getOption("print_level")=="debug") printlevel = ACADO::DEBUG;
else throw CasadiException("Illegal print level. Allowed are \"none\", \"low\", \"medium\", \"high\", \"debug\"");
algorithm_->set(ACADO::INTEGRATOR_PRINTLEVEL, printlevel );
// Set integrator
if(hasSetOption("integrator")){
GenericType integ = getOption("integrator");
ACADO::IntegratorType itype;
if(integ=="rk4") itype=ACADO::INT_RK4;
else if(integ=="rk12") itype=ACADO::INT_RK12;
else if(integ=="rk23") itype=ACADO::INT_RK23;
else if(integ=="rk45") itype=ACADO::INT_RK45;
else if(integ=="rk78") itype=ACADO::INT_RK78;
else if(integ=="bdf") itype=ACADO::INT_BDF;
else if(integ=="discrete") itype=ACADO::INT_DISCRETE;
else if(integ=="unknown") itype=ACADO::INT_UNKNOWN;
#ifdef ACADO_HAS_USERDEF_INTEGRATOR
else if(integ=="casadi"){
if(ACADO::Integrator::integrator_creator_ || ACADO::Integrator::integrator_user_data_)
throw CasadiException("AcadoOCPInternal::AcadoOCPInternal: An instance already exists");
if(integrators_.size() <= n_nodes_)
throw CasadiException("AcadoOCPInternal::AcadoOCPInternal: Number of integrators does not match number of shooting nodes");
ACADO::Integrator::integrator_creator_ = &AcadoIntegratorBackend::create;
ACADO::Integrator::integrator_user_data_ = this;
itype=ACADO::INT_UNKNOWN;
}
#endif
else throw CasadiException("AcadoOCPInternal::evaluate: no such integrator: " + integ.toString());
algorithm_->set(ACADO::INTEGRATOR_TYPE, itype);
};
// Set integrator tolerance
if(hasSetOption("integrator_tolerance")) algorithm_->set( ACADO::INTEGRATOR_TOLERANCE, getOption("integrator_tolerance").toDouble());
if(hasSetOption("absolute_tolerance")) algorithm_->set( ACADO::ABSOLUTE_TOLERANCE, getOption("absolute_tolerance").toDouble());
if(hasSetOption("kkt_tolerance")) algorithm_->set( ACADO::KKT_TOLERANCE, getOption("kkt_tolerance").toDouble());
if(hasSetOption("max_num_iterations")) algorithm_->set( ACADO::MAX_NUM_ITERATIONS, getOption("max_num_iterations").toInt() );
if(hasSetOption("max_num_integrator_steps")) algorithm_->set( ACADO::MAX_NUM_INTEGRATOR_STEPS, getOption("max_num_integrator_steps").toInt() );
if(hasSetOption("relaxation_parameter")) algorithm_->set( ACADO::RELAXATION_PARAMETER, getOption("relaxation_parameter").toDouble());
if(hasSetOption("dynamic_sensitivity")){
if(getOption("dynamic_sensitivity") == "forward_sensitivities")
algorithm_->set( ACADO::DYNAMIC_SENSITIVITY, ACADO::FORWARD_SENSITIVITY );
else if(getOption("dynamic_sensitivity") == "backward_sensitivities")
algorithm_->set( ACADO::DYNAMIC_SENSITIVITY, ACADO::BACKWARD_SENSITIVITY );
else
throw CasadiException("illegal dynamic_sensitivity");
}
if(hasSetOption("hessian_approximation")){
int hess;
GenericType op = getOption("hessian_approximation");
if(op=="exact_hessian") hess = ACADO::EXACT_HESSIAN;
else if(op == "constant_hessian") hess = ACADO::CONSTANT_HESSIAN;
else if(op == "full_bfgs_update") hess = ACADO::FULL_BFGS_UPDATE;
else if(op == "block_bfgs_update") hess = ACADO::BLOCK_BFGS_UPDATE;
else if(op == "gauss_newton") hess = ACADO::GAUSS_NEWTON;
else if(op == "gauss_newton_with_block_bfgs") hess = ACADO::GAUSS_NEWTON_WITH_BLOCK_BFGS;
else throw CasadiException("illegal hessian approximation");
algorithm_->set( ACADO::HESSIAN_APPROXIMATION, hess);
}
// should the states be initialized by a forward integration?
bool auto_init = getOption("auto_init").toInt();
// Initialize differential states
if(nxd_>0){
// Initial guess
Matrix<double> &x0 = input(ACADO_X_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid xd(nxd_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(nxd_,&x0.at(i*nx_));
xd.setVector(i,v);
}
// Pass to acado
algorithm_->initializeDifferentialStates(xd,auto_init ? ACADO::BT_TRUE : ACADO::BT_FALSE);
}
// Initialize algebraic states
if(nxa_>0){
// Initial guess
Matrix<double> &x0 = input(ACADO_X_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid xa(nxa_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(nxa_,&x0.at(i*nx_+nxd_));
xa.setVector(i,v);
}
// Pass to acado
algorithm_->initializeAlgebraicStates(xa,auto_init ? ACADO::BT_TRUE : ACADO::BT_FALSE);
}
// Initialize controls
if(nu_>0){
// Initial guess
Matrix<double> &u0 = input(ACADO_U_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid u(nu_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(nu_,&u0.at(i*nu_));
u.setVector(i,v);
}
// Pass to acado
algorithm_->initializeControls(u);
}
// Initialize parameters
if(np_>0){
// Initial guess
Matrix<double> &p0 = input(ACADO_P_GUESS);
// Assemble the variables grid
ACADO::VariablesGrid p(np_, n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
ACADO::Vector v(np_,&p0.front()); // NB!
p.setVector(i,v);
}
// Pass to acado
algorithm_->initializeParameters(p);
}
// Solve
algorithm_->solve();
// Get the optimal state trajectory
if(nxd_>0){
Matrix<double> &xopt = output(ACADO_X_OPT);
ACADO::VariablesGrid xd;
algorithm_->getDifferentialStates(xd);
assert(xd.getNumPoints()==n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
// Copy to result
ACADO::Vector v = xd.getVector(i);
&xopt.at(i*nx_) << v;
}
}
if(nxa_>0){
Matrix<double> &xopt = output(ACADO_X_OPT);
ACADO::VariablesGrid xa;
algorithm_->getAlgebraicStates(xa);
assert(xa.getNumPoints()==n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
// Copy to result
ACADO::Vector v = xa.getVector(i);
&xopt.at(i*nx_ + nxd_) << v;
}
}
// Get the optimal control trajectory
if(nu_>0){
Matrix<double> &uopt = output(ACADO_U_OPT);
ACADO::VariablesGrid u;
algorithm_->getControls(u);
assert(u.getNumPoints()==n_nodes_+1);
for(int i=0; i<n_nodes_+1; ++i){
// Copy to result
ACADO::Vector v = u.getVector(i);
&uopt.at(i*nu_) << v;
}
}
// Get the optimal parameters
if(np_>0){
Matrix<double> &popt = output(ACADO_P_OPT);
ACADO::Vector p;
algorithm_->getParameters(p);
&popt.front() << p;
}
// Get the optimal cost
double cost = algorithm_->getObjectiveValue();
output(ACADO_COST).set(cost);
}
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();
}
void SQPInternal::init(){
// Call the init method of the base class
NLPSolverInternal::init();
// Read options
maxiter_ = getOption("maxiter");
maxiter_ls_ = getOption("maxiter_ls");
c1_ = getOption("c1");
beta_ = getOption("beta");
merit_memsize_ = getOption("merit_memory");
lbfgs_memory_ = getOption("lbfgs_memory");
tol_pr_ = getOption("tol_pr");
tol_du_ = getOption("tol_du");
regularize_ = getOption("regularize");
if(getOption("hessian_approximation")=="exact")
hess_mode_ = HESS_EXACT;
else if(getOption("hessian_approximation")=="limited-memory")
hess_mode_ = HESS_BFGS;
if (hess_mode_== HESS_EXACT && H_.isNull()) {
if (!getOption("generate_hessian")){
casadi_error("SQPInternal::evaluate: you set option 'hessian_approximation' to 'exact', but no hessian was supplied. Try with option \"generate_hessian\".");
}
}
// If the Hessian is generated, we use exact approximation by default
if (bool(getOption("generate_hessian"))){
setOption("hessian_approximation", "exact");
}
// Allocate a QP solver
CRSSparsity H_sparsity = hess_mode_==HESS_EXACT ? H_.output().sparsity() : sp_dense(n_,n_);
H_sparsity = H_sparsity + DMatrix::eye(n_).sparsity();
CRSSparsity A_sparsity = J_.isNull() ? CRSSparsity(0,n_,false) : J_.output().sparsity();
QPSolverCreator qp_solver_creator = getOption("qp_solver");
qp_solver_ = qp_solver_creator(H_sparsity,A_sparsity);
// Set options if provided
if(hasSetOption("qp_solver_options")){
Dictionary qp_solver_options = getOption("qp_solver_options");
qp_solver_.setOption(qp_solver_options);
}
qp_solver_.init();
// Lagrange multipliers of the NLP
mu_.resize(m_);
mu_x_.resize(n_);
// Lagrange gradient in the next iterate
gLag_.resize(n_);
gLag_old_.resize(n_);
// Current linearization point
x_.resize(n_);
x_cand_.resize(n_);
x_old_.resize(n_);
// Constraint function value
gk_.resize(m_);
gk_cand_.resize(m_);
// Hessian approximation
Bk_ = DMatrix(H_sparsity);
// Jacobian
Jk_ = DMatrix(A_sparsity);
// Bounds of the QP
qp_LBA_.resize(m_);
qp_UBA_.resize(m_);
qp_LBX_.resize(n_);
qp_UBX_.resize(n_);
// QP solution
dx_.resize(n_);
qp_DUAL_X_.resize(n_);
qp_DUAL_A_.resize(m_);
// Gradient of the objective
gf_.resize(n_);
// Create Hessian update function
if(hess_mode_ == HESS_BFGS){
// Create expressions corresponding to Bk, x, x_old, gLag and gLag_old
SXMatrix Bk = ssym("Bk",H_sparsity);
SXMatrix x = ssym("x",input(NLP_X_INIT).sparsity());
SXMatrix x_old = ssym("x",x.sparsity());
SXMatrix gLag = ssym("gLag",x.sparsity());
SXMatrix gLag_old = ssym("gLag_old",x.sparsity());
SXMatrix sk = x - x_old;
SXMatrix yk = gLag - gLag_old;
SXMatrix qk = mul(Bk, sk);
// Calculating theta
SXMatrix skBksk = inner_prod(sk, qk);
SXMatrix omega = if_else(inner_prod(yk, sk) < 0.2 * inner_prod(sk, qk),
0.8 * skBksk / (skBksk - inner_prod(sk, yk)),
1);
yk = omega * yk + (1 - omega) * qk;
SXMatrix theta = 1. / inner_prod(sk, yk);
SXMatrix phi = 1. / inner_prod(qk, sk);
SXMatrix Bk_new = Bk + theta * mul(yk, trans(yk)) - phi * mul(qk, trans(qk));
// Inputs of the BFGS update function
vector<SXMatrix> bfgs_in(BFGS_NUM_IN);
bfgs_in[BFGS_BK] = Bk;
bfgs_in[BFGS_X] = x;
bfgs_in[BFGS_X_OLD] = x_old;
bfgs_in[BFGS_GLAG] = gLag;
bfgs_in[BFGS_GLAG_OLD] = gLag_old;
bfgs_ = SXFunction(bfgs_in,Bk_new);
bfgs_.setOption("number_of_fwd_dir",0);
bfgs_.setOption("number_of_adj_dir",0);
bfgs_.init();
// Initial Hessian approximation
B_init_ = DMatrix::eye(n_);
}
// Header
if(bool(getOption("print_header"))){
cout << "-------------------------------------------" << endl;
cout << "This is CasADi::SQPMethod." << endl;
switch (hess_mode_) {
case HESS_EXACT:
cout << "Using exact Hessian" << endl;
break;
case HESS_BFGS:
cout << "Using limited memory BFGS Hessian approximation" << endl;
break;
}
cout << endl;
cout << "Number of variables: " << setw(9) << n_ << endl;
cout << "Number of constraints: " << setw(9) << m_ << endl;
cout << "Number of nonzeros in constraint Jacobian: " << setw(9) << A_sparsity.size() << endl;
cout << "Number of nonzeros in Lagrangian Hessian: " << setw(9) << H_sparsity.size() << endl;
cout << endl;
}
}
void CollocationIntegratorInternal::init(){
// Call the base class init
IntegratorInternal::init();
// Legendre collocation points
double legendre_points[][6] = {
{0},
{0,0.500000},
{0,0.211325,0.788675},
{0,0.112702,0.500000,0.887298},
{0,0.069432,0.330009,0.669991,0.930568},
{0,0.046910,0.230765,0.500000,0.769235,0.953090}};
// Radau collocation points
double radau_points[][6] = {
{0},
{0,1.000000},
{0,0.333333,1.000000},
{0,0.155051,0.644949,1.000000},
{0,0.088588,0.409467,0.787659,1.000000},
{0,0.057104,0.276843,0.583590,0.860240,1.000000}};
// Read options
bool use_radau;
if(getOption("collocation_scheme")=="radau"){
use_radau = true;
} else if(getOption("collocation_scheme")=="legendre"){
use_radau = false;
}
// Hotstart?
hotstart_ = getOption("hotstart");
// Number of finite elements
int nk = getOption("number_of_finite_elements");
// Interpolation order
int deg = getOption("interpolation_order");
// Assume explicit ODE
bool explicit_ode = f_.input(DAE_XDOT).size()==0;
// All collocation time points
double* tau_root = use_radau ? radau_points[deg] : legendre_points[deg];
// Size of the finite elements
double h = (tf_-t0_)/nk;
// MX version of the same
MX h_mx = h;
// Coefficients of the collocation equation
vector<vector<MX> > C(deg+1,vector<MX>(deg+1));
// Coefficients of the continuity equation
vector<MX> D(deg+1);
// Collocation point
SXMatrix tau = ssym("tau");
// For all collocation points
for(int j=0; j<deg+1; ++j){
// Construct Lagrange polynomials to get the polynomial basis at the collocation point
SXMatrix L = 1;
for(int j2=0; j2<deg+1; ++j2){
if(j2 != j){
L *= (tau-tau_root[j2])/(tau_root[j]-tau_root[j2]);
}
}
SXFunction lfcn(tau,L);
lfcn.init();
// Evaluate the polynomial at the final time to get the coefficients of the continuity equation
lfcn.setInput(1.0);
lfcn.evaluate();
D[j] = lfcn.output();
// Evaluate the time derivative of the polynomial at all collocation points to get the coefficients of the continuity equation
for(int j2=0; j2<deg+1; ++j2){
lfcn.setInput(tau_root[j2]);
lfcn.setFwdSeed(1.0);
lfcn.evaluate(1,0);
C[j][j2] = lfcn.fwdSens();
}
}
// Initial state
MX X0("X0",nx_);
// Parameters
MX P("P",np_);
// Backward state
MX RX0("RX0",nrx_);
// Backward parameters
MX RP("RP",nrp_);
// Collocated differential states and algebraic variables
int nX = (nk*(deg+1)+1)*(nx_+nrx_);
int nZ = nk*deg*(nz_+nrz_);
// Unknowns
MX V("V",nX+nZ);
int offset = 0;
// Get collocated states, algebraic variables and times
vector<vector<MX> > X(nk+1);
vector<vector<MX> > RX(nk+1);
vector<vector<MX> > Z(nk);
vector<vector<MX> > RZ(nk);
coll_time_.resize(nk+1);
for(int k=0; k<nk+1; ++k){
// Number of time points
int nj = k==nk ? 1 : deg+1;
// Allocate differential states expressions at the time points
X[k].resize(nj);
RX[k].resize(nj);
coll_time_[k].resize(nj);
// Allocate algebraic variable expressions at the collocation points
if(k!=nk){
Z[k].resize(nj-1);
RZ[k].resize(nj-1);
}
// For all time points
for(int j=0; j<nj; ++j){
// Get expressions for the differential state
X[k][j] = V[range(offset,offset+nx_)];
offset += nx_;
RX[k][j] = V[range(offset,offset+nrx_)];
offset += nrx_;
// Get the local time
coll_time_[k][j] = h*(k + tau_root[j]);
// Get expressions for the algebraic variables
if(j>0){
Z[k][j-1] = V[range(offset,offset+nz_)];
offset += nz_;
RZ[k][j-1] = V[range(offset,offset+nrz_)];
offset += nrz_;
}
}
}
// Check offset for consistency
casadi_assert(offset==V.size());
// Constraints
vector<MX> g;
g.reserve(2*(nk+1));
// Quadrature expressions
MX QF = MX::zeros(nq_);
MX RQF = MX::zeros(nrq_);
// Counter
int jk = 0;
// Add initial condition
g.push_back(X[0][0]-X0);
// For all finite elements
for(int k=0; k<nk; ++k, ++jk){
// For all collocation points
for(int j=1; j<deg+1; ++j, ++jk){
// Get the time
MX tkj = coll_time_[k][j];
// Get an expression for the state derivative at the collocation point
MX xp_jk = 0;
for(int j2=0; j2<deg+1; ++j2){
xp_jk += C[j2][j]*X[k][j2];
}
// Add collocation equations to the NLP
vector<MX> f_in(DAE_NUM_IN);
f_in[DAE_T] = tkj;
f_in[DAE_P] = P;
f_in[DAE_X] = X[k][j];
f_in[DAE_Z] = Z[k][j-1];
vector<MX> f_out;
if(explicit_ode){
// Assume equation of the form ydot = f(t,y,p)
f_out = f_.call(f_in);
g.push_back(h_mx*f_out[DAE_ODE] - xp_jk);
} else {
// Assume equation of the form 0 = f(t,y,ydot,p)
f_in[DAE_XDOT] = xp_jk/h_mx;
f_out = f_.call(f_in);
g.push_back(f_out[DAE_ODE]);
}
// Add the algebraic conditions
if(nz_>0){
g.push_back(f_out[DAE_ALG]);
}
// Add the quadrature
if(nq_>0){
QF += D[j]*h_mx*f_out[DAE_QUAD];
}
// Now for the backward problem
if(nrx_>0){
// Get an expression for the state derivative at the collocation point
MX rxp_jk = 0;
for(int j2=0; j2<deg+1; ++j2){
rxp_jk += C[j2][j]*RX[k][j2];
}
// Add collocation equations to the NLP
vector<MX> g_in(RDAE_NUM_IN);
g_in[RDAE_T] = tkj;
g_in[RDAE_X] = X[k][j];
g_in[RDAE_Z] = Z[k][j-1];
g_in[RDAE_P] = P;
g_in[RDAE_RP] = RP;
g_in[RDAE_RX] = RX[k][j];
g_in[RDAE_RZ] = RZ[k][j-1];
vector<MX> g_out;
if(explicit_ode){
// Assume equation of the form xdot = f(t,x,p)
g_out = g_.call(g_in);
g.push_back(h_mx*g_out[RDAE_ODE] - rxp_jk);
} else {
// Assume equation of the form 0 = f(t,x,xdot,p)
g_in[RDAE_XDOT] = xp_jk/h_mx;
g_in[RDAE_RXDOT] = rxp_jk/h_mx;
g_out = g_.call(g_in);
g.push_back(g_out[RDAE_ODE]);
}
// Add the algebraic conditions
if(nrz_>0){
g.push_back(g_out[RDAE_ALG]);
}
// Add the backward quadrature
if(nrq_>0){
RQF += D[j]*h_mx*g_out[RDAE_QUAD];
}
}
}
// Get an expression for the state at the end of the finite element
MX xf_k = 0;
for(int j=0; j<deg+1; ++j){
xf_k += D[j]*X[k][j];
}
// Add continuity equation to NLP
g.push_back(X[k+1][0] - xf_k);
if(nrx_>0){
// Get an expression for the state at the end of the finite element
MX rxf_k = 0;
for(int j=0; j<deg+1; ++j){
rxf_k += D[j]*RX[k][j];
}
// Add continuity equation to NLP
g.push_back(RX[k+1][0] - rxf_k);
}
}
// Add initial condition for the backward integration
if(nrx_>0){
g.push_back(RX[nk][0]-RX0);
}
// Constraint expression
MX gv = vertcat(g);
// Make sure that the dimension is consistent with the number of unknowns
casadi_assert_message(gv.size()==V.size(),"Implicit function unknowns and equations do not match");
// Nonlinear constraint function input
vector<MX> gfcn_in(1+INTEGRATOR_NUM_IN);
gfcn_in[0] = V;
gfcn_in[1+INTEGRATOR_X0] = X0;
gfcn_in[1+INTEGRATOR_P] = P;
gfcn_in[1+INTEGRATOR_RX0] = RX0;
gfcn_in[1+INTEGRATOR_RP] = RP;
vector<MX> gfcn_out(1+INTEGRATOR_NUM_OUT);
gfcn_out[0] = gv;
gfcn_out[1+INTEGRATOR_XF] = X[nk][0];
gfcn_out[1+INTEGRATOR_QF] = QF;
gfcn_out[1+INTEGRATOR_RXF] = RX[0][0];
gfcn_out[1+INTEGRATOR_RQF] = RQF;
// Nonlinear constraint function
FX gfcn = MXFunction(gfcn_in,gfcn_out);
// Expand f?
bool expand_f = getOption("expand_f");
if(expand_f){
gfcn.init();
gfcn = SXFunction(shared_cast<MXFunction>(gfcn));
}
// Get the NLP creator function
implicitFunctionCreator implicit_function_creator = getOption("implicit_solver");
// Allocate an NLP solver
implicit_solver_ = implicit_function_creator(gfcn);
// Pass options
if(hasSetOption("implicit_solver_options")){
const Dictionary& implicit_solver_options = getOption("implicit_solver_options");
implicit_solver_.setOption(implicit_solver_options);
}
// Initialize the solver
implicit_solver_.init();
if(hasSetOption("startup_integrator")){
// Create the linear solver
integratorCreator startup_integrator_creator = getOption("startup_integrator");
// Allocate an NLP solver
startup_integrator_ = startup_integrator_creator(f_,g_);
// Pass options
startup_integrator_.setOption("number_of_fwd_dir",0); // not needed
startup_integrator_.setOption("number_of_adj_dir",0); // not needed
startup_integrator_.setOption("t0",coll_time_.front().front());
startup_integrator_.setOption("tf",coll_time_.back().back());
if(hasSetOption("startup_integrator_options")){
const Dictionary& startup_integrator_options = getOption("startup_integrator_options");
startup_integrator_.setOption(startup_integrator_options);
}
// Initialize the startup integrator
startup_integrator_.init();
}
// Mark the system not yet integrated
integrated_once_ = false;
}
void DirectSingleShootingInternal::init(){
// Initialize the base classes
OCPSolverInternal::init();
// Create an integrator instance
integratorCreator integrator_creator = getOption("integrator");
integrator_ = integrator_creator(ffcn_,FX());
if(hasSetOption("integrator_options")){
integrator_.setOption(getOption("integrator_options"));
}
// Set t0 and tf
integrator_.setOption("t0",0);
integrator_.setOption("tf",tf_/nk_);
integrator_.init();
// Path constraints present?
bool path_constraints = nh_>0;
// Count the total number of NLP variables
int NV = np_ + // global parameters
nx_ + // initial state
nu_*nk_; // local control
// Declare variable vector for the NLP
// The structure is as follows:
// np x 1 (parameters)
// ------
// nx x 1 (states at time i=0)
// ------
// nu x 1 (controls in interval i=0)
// .....
// nx x 1 (controls in interval i=nk-1)
MX V = msym("V",NV);
int offset = 0;
// Global parameters
MX P = V[Slice(0,np_)];
offset += np_;
// Initial state
MX X0 = V[Slice(offset,offset+nx_)];
offset += nx_;
// Control for each shooting interval
vector<MX> U(nk_);
for(int k=0; k<nk_; ++k){ // interior nodes
U[k] = V[range(offset,offset+nu_)];
offset += nu_;
}
// Make sure that the size of the variable vector is consistent with the number of variables that we have referenced
casadi_assert(offset==NV);
// Current state
MX X = X0;
// Objective
MX nlp_j = 0;
// Constraints
vector<MX> nlp_g;
nlp_g.reserve(nk_*(path_constraints ? 2 : 1));
// For all shooting nodes
for(int k=0; k<nk_; ++k){
// Integrate
vector<MX> int_out = integrator_.call(integratorIn("x0",X,"p",vertcat(P,U[k])));
// Store expression for state trajectory
X = int_out[INTEGRATOR_XF];
// Add constraints on the state
nlp_g.push_back(X);
// Add path constraints
if(path_constraints){
vector<MX> cfcn_out = cfcn_.call(daeIn("x",X,"p",U[k])); // TODO: Change signature of cfcn_: remove algebraic variable, add control
nlp_g.push_back(cfcn_out.at(0));
}
}
// Terminal constraints
G_ = MXFunction(V,vertcat(nlp_g));
G_.setOption("name","nlp_g");
G_.init();
// Objective function
MX jk = mfcn_.call(mayerIn("x",X,"p",P)).at(0);
nlp_j += jk;
F_ = MXFunction(V,nlp_j);
F_.setOption("name","nlp_j");
// Get the NLP creator function
NLPSolverCreator nlp_solver_creator = getOption("nlp_solver");
// Allocate an NLP solver
nlp_solver_ = nlp_solver_creator(F_,G_,FX(),FX());
// Pass options
if(hasSetOption("nlp_solver_options")){
const Dictionary& nlp_solver_options = getOption("nlp_solver_options");
nlp_solver_.setOption(nlp_solver_options);
}
// Initialize the solver
nlp_solver_.init();
}
void ImplicitFunctionInternal::init() {
// Initialize the residual function
f_.init(false);
// Which input/output correspond to the root-finding problem?
iin_ = getOption("implicit_input");
iout_ = getOption("implicit_output");
// Get the number of equations and check consistency
casadi_assert_message(iin_>=0 && iin_<f_.getNumInputs() && f_.getNumInputs()>0,
"Implicit input not in range");
casadi_assert_message(iout_>=0 && iout_<f_.getNumOutputs() && f_.getNumOutputs()>0,
"Implicit output not in range");
casadi_assert_message(f_.output(iout_).isDense() && f_.output(iout_).isVector(),
"Residual must be a dense vector");
casadi_assert_message(f_.input(iin_).isDense() && f_.input(iin_).isVector(),
"Unknown must be a dense vector");
n_ = f_.output(iout_).size();
casadi_assert_message(n_ == f_.input(iin_).size(),
"Dimension mismatch. Input size is "
<< f_.input(iin_).size()
<< ", while output size is "
<< f_.output(iout_).size());
// Allocate inputs
setNumInputs(f_.getNumInputs());
for (int i=0; i<getNumInputs(); ++i) {
input(i) = f_.input(i);
}
// Allocate output
setNumOutputs(f_.getNumOutputs());
for (int i=0; i<getNumOutputs(); ++i) {
output(i) = f_.output(i);
}
// Same input and output schemes
setInputScheme(f_.getInputScheme());
setOutputScheme(f_.getOutputScheme());
// Call the base class initializer
FunctionInternal::init();
// Generate Jacobian if not provided
if (jac_.isNull()) jac_ = f_.jacobian(iin_, iout_);
jac_.init(false);
// Check for structural singularity in the Jacobian
casadi_assert_message(
!jac_.output().sparsity().isSingular(),
"ImplicitFunctionInternal::init: singularity - the jacobian is structurally rank-deficient. "
"sprank(J)=" << sprank(jac_.output()) << " (instead of "<< jac_.output().size1() << ")");
// Get the linear solver creator function
if (linsol_.isNull()) {
if (hasSetOption("linear_solver")) {
std::string linear_solver_name = getOption("linear_solver");
// Allocate an NLP solver
linsol_ = LinearSolver(linear_solver_name, jac_.output().sparsity(), 1);
// Pass options
if (hasSetOption("linear_solver_options")) {
const Dictionary& linear_solver_options = getOption("linear_solver_options");
linsol_.setOption(linear_solver_options);
}
// Initialize
linsol_.init();
}
} else {
// Initialize the linear solver, if provided
linsol_.init(false);
casadi_assert(linsol_.input().sparsity()==jac_.output().sparsity());
}
// No factorization yet;
fact_up_to_date_ = false;
// Constraints
if (hasSetOption("constraints")) u_c_ = getOption("constraints");
casadi_assert_message(u_c_.size()==n_ || u_c_.empty(),
"Constraint vector if supplied, must be of length n, but got "
<< u_c_.size() << " and n = " << n_);
}
void LiftingLrDpleInternal::init() {
form_ = getOptionEnumValue("form");
// Initialize the base classes
LrDpleInternal::init();
casadi_assert_message(!pos_def_,
"pos_def option set to True: Solver only handles the indefinite case.");
casadi_assert_message(const_dim_,
"const_dim option set to False: Solver only handles the True case.");
// We will construct an MXFunction to facilitate the calculation of derivatives
MX As = MX::sym("As", input(LR_DLE_A).sparsity());
MX Vs = MX::sym("Vs", input(LR_DLE_V).sparsity());
MX Cs = MX::sym("Cs", input(LR_DLE_C).sparsity());
MX Hs = MX::sym("Hs", input(LR_DLE_H).sparsity());
n_ = A_[0].size1();
// Chop-up the arguments
std::vector<MX> As_ = horzsplit(As, n_);
std::vector<MX> Vs_ = horzsplit(Vs, V_[0].size2());
std::vector<MX> Cs_ = horzsplit(Cs, V_[0].size2());
std::vector<MX> Hs_;
if (with_H_) {
Hs_ = horzsplit(Hs, Hsi_);
}
MX A;
if (K_==1) {
A = As;
} else {
if (form_==0) {
MX AL = diagcat(vector_slice(As_, range(As_.size()-1)));
MX AL2 = horzcat(AL, MX::sparse(AL.size1(), As_[0].size2()));
MX AT = horzcat(MX::sparse(As_[0].size1(), AL.size2()), As_.back());
A = vertcat(AT, AL2);
} else {
MX AL = diagcat(reverse(vector_slice(As_, range(As_.size()-1))));
MX AL2 = horzcat(MX::sparse(AL.size1(), As_[0].size2()), AL);
MX AT = horzcat(As_.back(), MX::sparse(As_[0].size1(), AL.size2()));
A = vertcat(AL2, AT);
}
}
MX V;
MX C;
MX H;
if (form_==0) {
V = diagcat(Vs_.back(), diagcat(vector_slice(Vs_, range(Vs_.size()-1))));
if (with_C_) {
C = diagcat(Cs_.back(), diagcat(vector_slice(Cs_, range(Cs_.size()-1))));
}
} else {
V = diagcat(diagcat(reverse(vector_slice(Vs_, range(Vs_.size()-1)))), Vs_.back());
if (with_C_) {
C = diagcat(diagcat(reverse(vector_slice(Cs_, range(Cs_.size()-1)))), Cs_.back());
}
}
if (with_H_) {
H = diagcat(form_==0? Hs_ : reverse(Hs_));
}
// Create an LrDleSolver instance
solver_ = LrDleSolver(getOption(solvername()),
lrdleStruct("a", A.sparsity(),
"v", V.sparsity(),
"c", C.sparsity(),
"h", H.sparsity()));
solver_.setOption("Hs", Hss_);
if (hasSetOption(optionsname())) solver_.setOption(getOption(optionsname()));
solver_.init();
std::vector<MX> v_in(LR_DPLE_NUM_IN);
v_in[LR_DLE_A] = As;
v_in[LR_DLE_V] = Vs;
if (with_C_) {
v_in[LR_DLE_C] = Cs;
}
if (with_H_) {
v_in[LR_DLE_H] = Hs;
}
std::vector<MX> Pr = solver_.call(lrdpleIn("a", A, "v", V, "c", C, "h", H));
MX Pf = Pr[0];
std::vector<MX> Ps = with_H_ ? diagsplit(Pf, Hsi_) : diagsplit(Pf, n_);
if (form_==1) {
Ps = reverse(Ps);
}
f_ = MXFunction(v_in, dpleOut("p", horzcat(Ps)));
f_.setInputScheme(SCHEME_LR_DPLEInput);
f_.setOutputScheme(SCHEME_LR_DPLEOutput);
f_.init();
Wrapper::checkDimensions();
}
void QpoasesInterface::init() {
QpSolverInternal::init();
// Read options
if (hasSetOption("nWSR")) {
max_nWSR_ = getOption("nWSR");
casadi_assert(max_nWSR_>=0);
} else {
max_nWSR_ = 5 *(n_ + nc_);
}
if (hasSetOption("CPUtime")) {
max_cputime_ = getOption("CPUtime");
casadi_assert(max_cputime_>0);
} else {
max_cputime_ = -1;
}
// Create data for H if not dense
if (!input(QP_SOLVER_H).sparsity().isDense()) h_data_.resize(n_*n_);
// Create data for A
a_data_.resize(n_*nc_);
// Dual solution vector
dual_.resize(n_+nc_);
// Create qpOASES instance
if (qp_) delete qp_;
if (ALLOW_QPROBLEMB && nc_==0) {
qp_ = new qpOASES::QProblemB(n_);
} else {
qp_ = new qpOASES::SQProblem(n_, nc_);
}
called_once_ = false;
#ifdef ALLOW_ALL_OPTIONS
qpOASES::Options ops;
ops.setToDefault();
ops.printLevel = string_to_PrintLevel(getOption("printLevel"));
ops.enableRamping = bool_to_BooleanType(getOption("enableRamping"));
ops.enableFarBounds = bool_to_BooleanType(getOption("enableFarBounds"));
ops.enableFlippingBounds = bool_to_BooleanType(getOption("enableFlippingBounds"));
ops.enableRegularisation = bool_to_BooleanType(getOption("enableRegularisation"));
ops.enableFullLITests = bool_to_BooleanType(getOption("enableFullLITests"));
ops.enableNZCTests = bool_to_BooleanType(getOption("enableNZCTests"));
ops.enableDriftCorrection = static_cast<int>(getOption("enableDriftCorrection"));
ops.enableCholeskyRefactorisation =
static_cast<int>(getOption("enableCholeskyRefactorisation"));
ops.enableEqualities = bool_to_BooleanType(getOption("enableEqualities"));
ops.terminationTolerance = getOption("terminationTolerance");
ops.boundTolerance = getOption("boundTolerance");
ops.boundRelaxation = getOption("boundRelaxation");
ops.epsNum = getOption("epsNum");
ops.epsDen = getOption("epsDen");
ops.maxPrimalJump = getOption("maxPrimalJump");
ops.maxDualJump = getOption("maxDualJump");
ops.initialRamping = getOption("initialRamping");
ops.finalRamping = getOption("finalRamping");
ops.initialFarBounds = getOption("initialFarBounds");
ops.growFarBounds = getOption("growFarBounds");
ops.initialStatusBounds = string_to_SubjectToStatus(getOption("initialStatusBounds"));
ops.epsFlipping = getOption("epsFlipping");
ops.numRegularisationSteps = static_cast<int>(getOption("numRegularisationSteps"));
ops.epsRegularisation = getOption("epsRegularisation");
ops.numRefinementSteps = static_cast<int>(getOption("numRefinementSteps"));
ops.epsIterRef = getOption("epsIterRef");
ops.epsLITests = getOption("epsLITests");
ops.epsNZCTests = getOption("epsNZCTests");
// Pass to qpOASES
qp_->setOptions(ops);
#else // ALLOW_ALL_OPTIONS
qp_->setPrintLevel(string_to_PrintLevel(getOption("printLevel")));
#endif // ALLOW_ALL_OPTIONS
}
void DirectMultipleShootingInternal::init(){
// Initialize the base classes
OCPSolverInternal::init();
// Create an integrator instance
integratorCreator integrator_creator = getOption("integrator");
integrator_ = integrator_creator(ffcn_,Function());
if(hasSetOption("integrator_options")){
integrator_.setOption(getOption("integrator_options"));
}
// Set t0 and tf
integrator_.setOption("t0",0);
integrator_.setOption("tf",tf_/nk_);
integrator_.init();
// Path constraints present?
bool path_constraints = nh_>0;
// Count the total number of NLP variables
int NV = np_ + // global parameters
nx_*(nk_+1) + // local state
nu_*nk_; // local control
// Declare variable vector for the NLP
// The structure is as follows:
// np x 1 (parameters)
// ------
// nx x 1 (states at time i=0)
// nu x 1 (controls in interval i=0)
// ------
// nx x 1 (states at time i=1)
// nu x 1 (controls in interval i=1)
// ------
// .....
// ------
// nx x 1 (states at time i=nk)
MX V = MX::sym("V",NV);
// Global parameters
MX P = V(Slice(0,np_));
// offset in the variable vector
int v_offset=np_;
// Disretized variables for each shooting node
vector<MX> X(nk_+1), U(nk_);
for(int k=0; k<=nk_; ++k){ // interior nodes
// Local state
X[k] = V[Slice(v_offset,v_offset+nx_)];
v_offset += nx_;
// Variables below do not appear at the end point
if(k==nk_) break;
// Local control
U[k] = V[Slice(v_offset,v_offset+nu_)];
v_offset += nu_;
}
// Make sure that the size of the variable vector is consistent with the number of variables that we have referenced
casadi_assert(v_offset==NV);
// Input to the parallel integrator evaluation
vector<vector<MX> > int_in(nk_);
for(int k=0; k<nk_; ++k){
int_in[k].resize(INTEGRATOR_NUM_IN);
int_in[k][INTEGRATOR_P] = vertcat(P,U[k]);
int_in[k][INTEGRATOR_X0] = X[k];
}
// Input to the parallel function evaluation
vector<vector<MX> > fcn_in(nk_);
for(int k=0; k<nk_; ++k){
fcn_in[k].resize(DAE_NUM_IN);
fcn_in[k][DAE_T] = (k*tf_)/nk_;
fcn_in[k][DAE_P] = vertcat(P,U.at(k));
fcn_in[k][DAE_X] = X[k];
}
// Options for the parallelizer
Dictionary paropt;
// Transmit parallelization mode
if(hasSetOption("parallelization"))
paropt["parallelization"] = getOption("parallelization");
// Evaluate function in parallel
vector<vector<MX> > pI_out = integrator_.callParallel(int_in,paropt);
// Evaluate path constraints in parallel
vector<vector<MX> > pC_out;
if(path_constraints)
pC_out = cfcn_.callParallel(fcn_in,paropt);
//Constraint function
vector<MX> gg(2*nk_);
// Collect the outputs
for(int k=0; k<nk_; ++k){
//append continuity constraints
gg[2*k] = pI_out[k][INTEGRATOR_XF] - X[k+1];
// append the path constraints
if(path_constraints)
gg[2*k+1] = pC_out[k][0];
}
// Terminal constraints
MX g = vertcat(gg);
// Objective function
MX f;
if (mfcn_.getNumInputs()==1) {
f = mfcn_(X.back()).front();
} else {
vector<MX> mfcn_argin(MAYER_NUM_IN);
mfcn_argin[MAYER_X] = X.back();
mfcn_argin[MAYER_P] = P;
f = mfcn_.call(mfcn_argin).front();
}
// NLP
nlp_ = MXFunction(nlpIn("x",V),nlpOut("f",f,"g",g));
nlp_.setOption("ad_mode","forward");
nlp_.init();
// Get the NLP creator function
NLPSolverCreator nlp_solver_creator = getOption("nlp_solver");
// Allocate an NLP solver
nlp_solver_ = nlp_solver_creator(nlp_);
// Pass user options
if(hasSetOption("nlp_solver_options")){
const Dictionary& nlp_solver_options = getOption("nlp_solver_options");
nlp_solver_.setOption(nlp_solver_options);
}
// Initialize the solver
nlp_solver_.init();
}