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ADOL-C_sparseNLP.cpp
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ADOL-C_sparseNLP.cpp
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#define SPARSE_HESS
//#define RETAPE
//#define DCOPT_DEBUG
#include <cassert>
#include "ADOL-C_sparseNLP.hpp"
double step = std::numeric_limits<double>::epsilon();
time_t rand_time;
using namespace Ipopt;
#define tag_f 1
#define tag_g 2
#define tag_L 3
#define HPOFF 30
const char* nlp_solver(int enumval) {
const char* solver_string[] = {"ma27", "ma57", "ma86", "ma97", "mumps"};
return solver_string[enumval];
}
/* Constructor. */
MyADOLC_sparseNLP::MyADOLC_sparseNLP() {
cind_g = NULL;
rind_g = NULL;
jacval = NULL;
rind_L = NULL;
cind_L = NULL;
hessval = NULL;
nnz_jac = 0;
nnz_L = 0;
}
MyADOLC_sparseNLP::~MyADOLC_sparseNLP() {}
void MyADOLC_sparseNLP::set_endpoint_cost(double (*d_e_cost) (const double* ini_states, const double* fin_states, const double* param, const double& t0, const double& tf, Index phase, const double* constants),
adouble (*ad_e_cost) (const adouble* ini_states, const adouble* fin_states, const adouble* param, const adouble& t0, const adouble& tf, Index phase, const double* constants)) {
this->d_e_cost = d_e_cost;
this->ad_e_cost = ad_e_cost;
}
void MyADOLC_sparseNLP::set_derivatives(void (*d_derv)( double *states_dot, double *path, const double *states, const double *controls, const double *param, const double &time, Index phase, const double* constants),
void (*ad_derv)(adouble *states_dot, adouble *path, const adouble *states, const adouble *controls, const adouble *param, const adouble &time, Index phase, const double* constants)){
this->d_derv = d_derv;
this->ad_derv = ad_derv;
}
void MyADOLC_sparseNLP::set_events( void (*d_events)( double *events, const double *ini_states, const double *fin_states, const double *param, const double &t0, const double &tf, Index phase, const double* constants),
void (*ad_events)(adouble *events, const adouble *ini_states, const adouble *fin_states, const adouble *param, const adouble &t0, const adouble &tf, Index phase, const double* constants)) {
this->d_events = d_events;
this->ad_events = ad_events;
}
void MyADOLC_sparseNLP::mem_allocation(){
#ifdef DCOPT_DEBUG
cout<<"start mem_alloc\n";
#endif
if (n_states == 0 || n_nodes == 0) {
nlp_n = n_parameters;
nlp_m = n_events;
}
else {
if (config.disc_method == Hermite_Simpson){
nlp_n = (n_states + 2*n_controls)*n_nodes - n_controls + n_parameters + 2;
nlp_m = ((n_nodes - 1)*n_states + n_events + n_path_constraints*(2*n_nodes-1));
}
else {
nlp_n = (n_states + n_controls)*n_nodes + n_parameters + 2;
nlp_m = ((n_nodes - 1)*n_states + n_events + n_path_constraints*n_nodes);
}
}
lb_states.resize(n_states,1);
ub_states.resize(n_states,1);
lb_controls.resize(n_controls,1);
ub_controls.resize(n_controls,1);
lb_parameters.resize(n_parameters,1);
ub_parameters.resize(n_parameters,1);
lb_events.resize(n_events,1);
ub_events.resize(n_events,1);
lb_path.resize(n_path_constraints,1);
ub_path.resize(n_path_constraints,1);
nlp_guess_x = new double [nlp_n];
nlp_sf_x = new double [nlp_n];
nlp_sf_g = new double [nlp_m];
nlp_lb_x = new double[nlp_n];
nlp_ub_x = new double[nlp_n];
nlp_lb_g = new double[nlp_m];
nlp_ub_g = new double[nlp_m];
if (n_parameters) {
parameter_idx = new Index [n_parameters];
}
if (n_events) {
event_idx = new Index [n_events];
}
if (n_nodes && n_states) {
state_idx = new Index *[n_nodes];
defect_idx = new Index *[n_nodes - 1];
if (n_controls && config.disc_method == Hermite_Simpson) {
control_idx = new Index *[2*n_nodes-1];
path_constraint_idx = new Index *[2*n_nodes-1];
}
else {
control_idx = new Index *[n_nodes];
path_constraint_idx = new Index *[n_nodes];
}
}
for (Index i = 0; i < n_nodes; i++) {
if (n_states) {
state_idx[i] = new Index[n_states];
if (i < n_nodes - 1) {
defect_idx[i] = new Index [n_states];
}
}
if (n_controls && config.disc_method == Hermite_Simpson) {
control_idx[2*i] = new Index[n_controls];
if (n_path_constraints) {
path_constraint_idx[2*i] = new Index [n_path_constraints];
}
else {
path_constraint_idx[2*i] = NULL;
}
if (i < n_nodes - 1) {
control_idx[2*i+1] = new Index[n_controls];
if (n_path_constraints) {
path_constraint_idx[2*i+1] = new Index [n_path_constraints];
}
else {
path_constraint_idx[2*i+1] = NULL;
}
}
}
else if (n_controls) {
control_idx[i] = new Index[n_controls];
if (n_path_constraints) {
path_constraint_idx[i] = new Index [n_path_constraints];
}
else {
path_constraint_idx[i] = NULL;
}
}
}
#ifdef DCOPT_DEBUG
cout<<"end mem_alloc\n";
#endif
}
ApplicationReturnStatus MyADOLC_sparseNLP::initialization(SmartPtr<IpoptApplication> app){
#ifdef DCOPT_DEBUG
cout<<"start ini\n";
#endif
this->set_indexes();
this->set_sf();
this->set_bounces();
this->set_guess();
ApplicationReturnStatus status;
app->Options()->SetNumericValue("tol", config.NLP_tol);
app->Options()->SetNumericValue("constr_viol_tol",config.constr_viol_tol);
app->Options()->SetStringValue("mu_strategy", "adaptive");
app->Options()->SetStringValue("output_file", "ipopt.out");
// app->Options()->SetStringValue("nlp_scaling_method","gradient-based");
app->Options()->SetStringValue("linear_solver", nlp_solver(config.NLP_solver));// ma86 & ma57 with memmory leakage
app->Options()->SetIntegerValue("max_iter", config.max_iter);
app->Options()->SetIntegerValue("print_level", config.print_level);
app->Options()->SetIntegerValue("print_frequency_iter", config.print_freq);
if (config.warmstart) {
app->Options()->SetStringValue("warm_start_init_point", "yes");
}
app->Options()->SetNumericValue("mu_max",config.mu_max);
if (config.H_approximation){
app->Options()->SetStringValue("hessian_approximation", "limited-memory");
}
else {
app->Options()->SetStringValue("hessian_approximation", "exact");
}
if (config.warmstart) {
app->Options()->SetStringValue("warm_start_init_point","yes");
app->Options()->SetNumericValue("warm_start_bound_push",1e-4);
app->Options()->SetNumericValue("warm_start_mult_bound_push",1e-4);
app->Options()->SetNumericValue("mu_init",config.ipopt_mu_ini);
}
if (config.no_nlp_scaling)
app->Options()->SetStringValue("nlp_scaling_method","none");
status = app->Initialize();
if (status != Solve_Succeeded) {
printf("\n\n*** Error during initialization!\n");
}
return status;
#ifdef DCOPT_DEBUG
cout<<"end ini\n";
#endif
}
void MyADOLC_sparseNLP::set_indexes() {
#ifdef DCOPT_DEBUG
cout<<"start indexing\n";
#endif
Index idx_n = 0;
if (n_nodes == 0 || n_states == 0) {
for (Index i = 0; i < n_parameters; i++) {
parameter_idx[i] = idx_n;
idx_n++;
}
if (idx_n != nlp_n)
printf("something went wrong in setting indexes (static case)\n");
}
else {
for (Index i = 0; i < n_nodes; i++) {
for (Index j = 0; j < n_states; j++) {
state_idx[i][j] = idx_n;
idx_n++;
}
if (config.disc_method == Hermite_Simpson) {
for (Index j = 0; j < n_controls; j++) {
control_idx[2*i][j] = idx_n;
idx_n++;
if (i < n_nodes-1) {
control_idx[2*i+1][j] = idx_n;
idx_n++;
}
}
}
else {
for (Index j = 0; j < n_controls; j++) {
control_idx[i][j] = idx_n;
idx_n++;
}
}
}
for (Index i = 0; i < n_parameters; i++) {
parameter_idx[i] = idx_n;
idx_n++;
}
t0_idx = idx_n;
idx_n++;
tf_idx = idx_n;
idx_n++;
if (idx_n != nlp_n)
printf("something went wrong in setting indexes\n");
}
Index idx_m = 0;
if (n_nodes == 0 || n_states == 0) {
for (Index i = 0; i < n_events; i++) {
event_idx[i] = idx_m;
idx_m++;
}
if (idx_m != nlp_m)
printf("something went wrong in setting indexes (g static case)\n");
}
else {
for (Index i = 0; i < n_nodes; i++) {
if ( config.disc_method == Hermite_Simpson) {
for (Index j = 0; j < n_path_constraints; j++) {
path_constraint_idx[2*i][j] = idx_m;
idx_m++;
if (i < n_nodes - 1) {
path_constraint_idx[2*i+1][j] = idx_m;
idx_m++;
}
}
}
else {
for (Index j = 0; j < n_path_constraints; j++) {
path_constraint_idx[i][j] = idx_m;
idx_m++;
}
}
if ( i < n_nodes - 1) {
for (Index j = 0; j < n_states; j++) {
defect_idx[i][j] = idx_m;
idx_m++;
}
}
}
for (Index i = 0; i < n_events; i++) {
event_idx[i] = idx_m;
idx_m++;
}
if (idx_m != nlp_m)
printf("something went wrong in NLP_g_2_OCP_var\n");
}
#ifdef DCOPT_DEBUG
cout<<"end indexing\n";
#endif
}
void MyADOLC_sparseNLP::set_sf(){
#ifdef DCOPT_DEBUG
cout<<"start set_sf\n";
#endif
for (Index j = 1; j <= n_states; j++) {
if(lb_states(j) != 0 || ub_states(j) != 0) {
for (Index i = 0; i < n_nodes; i++)
nlp_sf_x[state_idx[i][j-1]] = max(fabs(lb_states(j)), fabs(ub_states(j)));
}
else {
for (Index i = 0; i < n_nodes; i++)
nlp_sf_x[state_idx[i][j-1]] = 1;
}
}
if (config.disc_method == Hermite_Simpson) {
for (Index j = 1; j <= n_controls; j++) {
if(lb_controls(j) != 0 || ub_controls(j) != 0) {
for (Index i = 0; i < 2*n_nodes - 1; i++) {
nlp_sf_x[control_idx[i][j-1]] = max(fabs(lb_controls(j)), fabs(ub_controls(j)));
}
}
else {
for (Index i = 0; i < 2*n_nodes - 1; i++) {
nlp_sf_x[control_idx[i][j-1]] = 1;
}
}
}
}
else {
for (Index j = 1; j <= n_controls; j++) {
if(lb_controls(j) != 0 || ub_controls(j) != 0) {
for (Index i = 0; i < n_nodes; i++) {
nlp_sf_x[control_idx[i][j-1]] = max(fabs(lb_controls(j)), fabs(ub_controls(j)));
}
}
else {
for (Index i = 0; i < n_nodes; i++) {
nlp_sf_x[control_idx[i][j-1]] = 1;
}
}
}
}
for (Index i = 1; i <= n_parameters; i++) {
if(lb_parameters(i) != 0 || ub_parameters(i) != 0) {
nlp_sf_x[parameter_idx[i-1]] = max(fabs(lb_parameters(i)), fabs(ub_parameters(i)));
}
else {
nlp_sf_x[parameter_idx[i-1]] = 1;
}
}
if (n_nodes && n_states) {
if(lb_t0 != 0 || ub_tf != 0) {
nlp_sf_x[t0_idx] = max(fabs(lb_t0(1)), fabs(ub_tf(1)));
nlp_sf_x[tf_idx] = max(fabs(lb_t0(1)), fabs(ub_tf(1)));
}
else {
nlp_sf_x[t0_idx] = 1;
nlp_sf_x[tf_idx] = 1;
}
}
if (config.disc_method == Hermite_Simpson) {
for (Index j = 1; j <= n_path_constraints; j++) {
if(lb_path(j) != 0 || ub_path(j) != 0) {
for (Index i = 0; i < 2*n_nodes-1; i++)
nlp_sf_g[path_constraint_idx[i][j-1]] = max(fabs(lb_path(j)), fabs(ub_path(j)));
}
else {
for (Index i = 0; i < n_nodes; i++)
nlp_sf_g[path_constraint_idx[i][j-1]] = 1;
}
}
}
else {
for (Index j = 1; j <= n_path_constraints; j++) {
if(lb_path(j) != 0 || ub_path(j) != 0) {
for (Index i = 0; i < n_nodes; i++)
nlp_sf_g[path_constraint_idx[i][j-1]] = max(fabs(lb_path(j)), fabs(ub_path(j)));
}
else {
for (Index i = 0; i < n_nodes; i++)
nlp_sf_g[path_constraint_idx[i][j-1]] = 1;
}
}
}
for (Index i = 0; i < n_nodes-1; i++)
for (Index j = 0; j < n_states; j++)
nlp_sf_g[defect_idx[i][j]] = 1;
for (Index j = 1; j <= n_events; j++) {
if(lb_events(j) != 0 || ub_events(j) != 0) {
nlp_sf_g[event_idx[j-1]] = max(fabs(lb_events(j)), fabs(ub_events(j)));
}
else {
nlp_sf_g[event_idx[j-1]] = 1;
}
}
#ifdef DCOPT_DEBUG
cout<<"end set_sf\n";
#endif
}
void MyADOLC_sparseNLP::set_bounces() {
for (Index i = 0; i < n_parameters; i++) {
nlp_lb_x[parameter_idx[i]] = lb_parameters(i+1)/nlp_sf_x[parameter_idx[i]];
nlp_ub_x[parameter_idx[i]] = ub_parameters(i+1)/nlp_sf_x[parameter_idx[i]];
}
if (n_nodes && n_states) {
nlp_lb_x[t0_idx] = lb_t0/nlp_sf_x[t0_idx];
nlp_lb_x[tf_idx] = lb_tf/nlp_sf_x[tf_idx];
nlp_ub_x[t0_idx] = ub_t0/nlp_sf_x[t0_idx];
nlp_ub_x[tf_idx] = ub_tf/nlp_sf_x[tf_idx];
}
for (Index i = 0; i < n_nodes; i++) {
for (Index j = 0; j < n_states; j++) {
nlp_lb_x[state_idx[i][j]] = lb_states(j+1)/nlp_sf_x[state_idx[i][j]];
nlp_ub_x[state_idx[i][j]] = ub_states(j+1)/nlp_sf_x[state_idx[i][j]];
}
if (config.disc_method == Hermite_Simpson) {
for (Index j = 0; j < n_controls; j++) {
nlp_lb_x[control_idx[2*i][j]] = lb_controls(j+1)/nlp_sf_x[control_idx[2*i][j]];
nlp_ub_x[control_idx[2*i][j]] = ub_controls(j+1)/nlp_sf_x[control_idx[2*i][j]];
if (i < n_nodes - 1) {
nlp_lb_x[control_idx[2*i+1][j]] = lb_controls(j+1)/nlp_sf_x[control_idx[2*i+1][j]];
nlp_ub_x[control_idx[2*i+1][j]] = ub_controls(j+1)/nlp_sf_x[control_idx[2*i+1][j]];
}
}
}
else {
for (Index j = 0; j < n_controls; j++) {
nlp_lb_x[control_idx[i][j]] = lb_controls(j+1)/nlp_sf_x[control_idx[i][j]];
nlp_ub_x[control_idx[i][j]] = ub_controls(j+1)/nlp_sf_x[control_idx[i][j]];
}
}
}
for (Index i = 0; i < n_nodes; i++) {
if (config.disc_method == Hermite_Simpson) {
for (Index j = 0; j < n_path_constraints; j++) {
nlp_lb_g[path_constraint_idx[2*i][j]] = lb_path(j+1)/nlp_sf_g[path_constraint_idx[2*i][j]];
nlp_ub_g[path_constraint_idx[2*i][j]] = ub_path(j+1)/nlp_sf_g[path_constraint_idx[2*i][j]];
if (i < n_nodes - 1) {
nlp_lb_g[path_constraint_idx[2*i+1][j]] = lb_path(j+1)/nlp_sf_g[path_constraint_idx[2*i+1][j]];
nlp_ub_g[path_constraint_idx[2*i+1][j]] = ub_path(j+1)/nlp_sf_g[path_constraint_idx[2*i+1][j]];
}
}
}
else {
for (Index j = 0; j < n_path_constraints; j++) {
nlp_lb_g[path_constraint_idx[i][j]] = lb_path(j+1)/nlp_sf_g[path_constraint_idx[i][j]];
nlp_ub_g[path_constraint_idx[i][j]] = ub_path(j+1)/nlp_sf_g[path_constraint_idx[i][j]];
}
}
if ( i < n_nodes - 1) {
for (Index j = 0; j < n_states; j++) {
nlp_lb_g[defect_idx[i][j]] = 0.0;
nlp_ub_g[defect_idx[i][j]] = 0.0;
}
}
}
for (Index i = 0; i < n_events; i++) {
nlp_lb_g[event_idx[i]] = lb_events(i+1)/nlp_sf_g[event_idx[i]];
nlp_ub_g[event_idx[i]] = ub_events(i+1)/nlp_sf_g[event_idx[i]];
}
}
void MyADOLC_sparseNLP::set_guess() {
#ifdef DCOPT_DEBUG
cout<<"set_guess\n";
#endif
if (config.warmstart) {
if (guess.z.getRowDim() == nlp_n && guess.lam_g.getRowDim() == nlp_m && guess.nodes.getRowDim() == n_nodes) {
double delta_t = guess.nodes(n_nodes,1) - guess.nodes(1,1);
node_str.resize(n_nodes-1,1);
for (Index i = 1; i <= n_nodes - 1; i++) {
node_str(i) = (guess.nodes(i+1,1) - guess.nodes(i,1))/delta_t;
}
nlp_guess_z_L = new double [nlp_n];
nlp_guess_z_U = new double [nlp_n];
nlp_guess_lam = new double [nlp_m];
for (Index i = 0; i < nlp_n; i++) {
nlp_guess_x[i] = guess.z(i+1,1);
nlp_guess_z_L[i] = guess.z_L(i+1,1);
nlp_guess_z_U[i] = guess.z_U(i+1,1);
}
for (Index i = 0; i < nlp_m; i++){
nlp_guess_lam[i] = guess.lam_g(i+1,1);
}
}
else{
cout<< "\n\nNo lambda provided\n\n";
exit(1);
}
}
else {
if (n_nodes == 0 || n_states == 0) {
if (guess.parameters.getRowDim() != (uint)n_parameters) {
cout<<"===== No guess provided or invalid guess =====\n"
"===== Generating guess based on Bounds =====\n\n";
guess_gen();
}
for (Index i = 0; i < n_parameters; i++) {
nlp_guess_x[parameter_idx[i]] = guess.parameters(i+1)/nlp_sf_x[parameter_idx[i]];
}
}
else {
if ((guess.nodes.getRowDim() != (uint)n_nodes) ||
(guess.parameters.getRowDim() != (uint)n_parameters) ||
((guess.u.getColDim() != (uint)n_controls) && guess.u_full.getColDim() != (uint)n_controls) ||
(guess.x.getColDim() != (uint)n_states) ||
((guess.u.getRowDim() != (uint)n_nodes) && guess.u_full.getRowDim() != (uint)(n_nodes*2-1)) ||
(guess.x.getRowDim() != (uint)n_nodes) ) {
cout<<"===== No guess provided or invalid guess =====\n"
"===== Generating guess based on Bounds =====\n\n";
guess_gen();
}
double delta_t = guess.nodes(n_nodes,1) - guess.nodes(1,1);
node_str.resize(n_nodes-1,1);
for (Index i = 1; i <= n_nodes - 1; i++) {
node_str(i) = (guess.nodes(i+1,1) - guess.nodes(i,1))/delta_t;
}
for (Index i = 0; i < n_parameters; i++) {
nlp_guess_x[parameter_idx[i]] = guess.parameters(i+1)/nlp_sf_x[parameter_idx[i]];
}
for (Index i = 0; i < n_nodes; i++) {
for (Index j = 0; j < n_states; j++) {
nlp_guess_x[state_idx[i][j]] = guess.x(i+1,j+1)/nlp_sf_x[state_idx[i][j]];
}
if (config.disc_method == Hermite_Simpson && guess.u_full.getColDim() == n_controls && guess.u_full.getRowDim() == (2*n_nodes - 1)) {
for (Index j = 0; j < n_controls; j++) {
nlp_guess_x[control_idx[2*i][j]] = guess.u_full(2*i+1,j+1)/nlp_sf_x[control_idx[2*i][j]];
if (i < n_nodes - 1) {
nlp_guess_x[control_idx[2*i][j]] = guess.u_full(2*i+2,j+1)/nlp_sf_x[control_idx[2*i+1][j]];
}
nlp_guess_x[control_idx[2*i][j]] = guess.u(i+1,j+1)/nlp_sf_x[control_idx[2*i][j]];
}
}
else if (config.disc_method == Hermite_Simpson){
for (Index j = 0; j < n_controls; j++) {
nlp_guess_x[control_idx[2*i][j]] = guess.u(i+1,j+1)/nlp_sf_x[control_idx[2*i][j]];
if (i < n_nodes - 1)
nlp_guess_x[control_idx[2*i+1][j]] = (guess.u(i+1,j+1)+guess.u(i+2,j+1))/(2*nlp_sf_x[control_idx[2*i+1][j]]);
}
}
else {
for (Index j = 0; j < n_controls; j++) {
nlp_guess_x[control_idx[i][j]] = guess.u(i+1,j+1)/nlp_sf_x[control_idx[i][j]];
}
}
}
nlp_guess_x[t0_idx] = guess.nodes(1,1)/nlp_sf_x[t0_idx];
nlp_guess_x[tf_idx] = guess.nodes(n_nodes,1)/nlp_sf_x[tf_idx];
}
}
#ifdef DCOPT_DEBUG
cout<<"end set_guess\n";
#endif
}
void MyADOLC_sparseNLP::guess_gen() {
#ifdef DCOPT_DEBUG
cout<<"guess generation\n";
#endif
if( n_nodes == 0 || n_states == 0) {
if(n_parameters > 0) {
guess.parameters.resize(n_parameters,1);
for (Index i = 1; i <= n_parameters; i++) {
guess.parameters(i) = (lb_parameters(i) + ub_parameters(i))/2;
}
}
}
else {
guess.nodes = linspace<double>((lb_t0+ub_t0)/2.0,(lb_tf+ub_tf)/2.0,n_nodes);
guess.x.resize(n_nodes, n_states);
for (Index i = 1; i <= n_states; i++) {
guess.x.setCol(i,linspace<double>(lb_states(i), ub_states(i),n_nodes));
}
if (n_controls) {
guess.u.resize(n_nodes,n_controls);
for (Index i = 1; i <= n_controls; i++) {
guess.u.setCol(i,linspace<double>(lb_controls(i), ub_controls(i),n_nodes));
}
if (config.disc_method == Hermite_Simpson) {
guess.u_full.resize(2*n_nodes-1,n_controls);
for (Index i = 1; i <= n_controls; i++) {
guess.u_full.setCol(i,linspace<double>(lb_controls(i), ub_controls(i),2*n_nodes-1));
}
}
}
else {
SMatrix<double> Null_vector;
guess.u = Null_vector;
}
if(n_parameters > 0) {
guess.parameters.resize(n_parameters,1);
for (Index i = 1; i <= n_parameters; i++) {
guess.parameters(i) = (double)(lb_parameters(i) + ub_parameters(i))/2;
}
}
}
#ifdef DCOPT_DEBUG
cout<<"end guess generation\n";
#endif
}
ApplicationReturnStatus MyADOLC_sparseNLP::solve(SmartPtr<IpoptApplication> app){
ApplicationReturnStatus status;
status = app->OptimizeTNLP(this);
if (status == Solve_Succeeded) {
// Retrieve some statistics about the solve
Index iter_count = app->Statistics()->IterationCount();
printf("\n\n*** The problem solved in %d iterations!\n", iter_count);
Number final_obj = app->Statistics()->FinalObjective();
printf("\n\n*** The final value of the objective function is %e.\n", final_obj);
}
return status;
}
bool MyADOLC_sparseNLP::ad_eval_obj(Index n, const adouble *z, adouble& obj_value) {
#ifdef DCOPT_DEBUG
printf("ad_eval_obj()\n");
#endif
adouble *d_z, *p_states_ini, *p_states_fin, *p_parameters, *p_t0, *p_tf;
d_z = new adouble [n];
for( Index i = 0; i < n; i++)
d_z[i] = z[i]*nlp_sf_x[i];
if (n_nodes != 0 && n_states != 0) {
p_states_ini = &d_z[state_idx[0][0]];
p_states_fin = &d_z[state_idx[n_nodes - 1][0]];
p_t0 = &d_z[t0_idx];
p_tf = &d_z[tf_idx];
}
else{
p_states_ini = NULL;
p_states_fin = NULL;
p_t0 = NULL;
p_tf = NULL;
}
if (n_parameters)
p_parameters = &d_z[parameter_idx[0]];
else
p_parameters = NULL;
obj_value = ad_e_cost (p_states_ini, p_states_fin, p_parameters, *p_t0, *p_tf, 1, constants);
delete[] d_z;
#ifdef DCOPT_DEBUG
printf("end of ad_eval_obj()\n");
#endif
return true;
}
bool MyADOLC_sparseNLP::eval_obj(Index n, const double *z, double& obj_value) {
// return the value of the objective function
#ifdef DCOPT_DEBUG
printf("eval_obj()\n");
#endif
double *d_z, *p_states_ini, *p_states_fin, *p_parameters, *p_t0, *p_tf;
d_z = new double [n];
for( Index i = 0; i < n; i++)
d_z[i] = z[i]*nlp_sf_x[i];
if (n_nodes != 0 && n_states != 0) {
p_states_ini = &d_z[state_idx[0][0]];
p_states_fin = &d_z[state_idx[n_nodes - 1][0]];
p_t0 = &d_z[t0_idx];
p_tf = &d_z[tf_idx];
}
else{
p_states_ini = NULL;
p_states_fin = NULL;
p_t0 = NULL;
p_tf = NULL;
}
if (n_parameters)
p_parameters = &d_z[parameter_idx[0]];
else
p_parameters = NULL;
obj_value = d_e_cost (p_states_ini, p_states_fin, p_parameters, *p_t0, *p_tf, 1, constants);
delete[] d_z;
#ifdef DCOPT_DEBUG
printf("end of eval_obj()\n");
#endif
return true;
}
bool MyADOLC_sparseNLP::ad_eval_constraints(Index n, const adouble *x, Index m, adouble* g) {
#ifdef DCOPT_DEBUG
printf("ad_eval_constraints()\n");
#endif
adouble *d_z, *p_controls, *p_states0,*p_states1, *p_states_ini, *p_states_fin, *p_parameters, *p_t0, *p_tf;
adouble *p_path = NULL, *p_events, *p_defects;
d_z = new adouble [n];
for( Index i = 0; i < n; i++)
d_z[i] = x[i]*nlp_sf_x[i];
if (n_events)
p_events = &g[event_idx[0]];
else
p_events = NULL;
if (n_nodes != 0 && n_states != 0) {
p_states_ini = &d_z[state_idx[0][0]];
p_states_fin = &d_z[state_idx[n_nodes - 1][0]];
p_t0 = &d_z[t0_idx];
p_tf = &d_z[tf_idx];
}
else{
p_states_ini = NULL;
p_states_fin = NULL;
p_t0 = NULL;
p_tf = NULL;
}
if (n_parameters)
p_parameters = &d_z[parameter_idx[0]];
else
p_parameters = NULL;
ad_events(p_events, p_states_ini, p_states_fin, p_parameters, *p_t0, *p_tf, 1, constants);
if(n_states != 0 && n_nodes != 0) {
adouble **f = new adouble *[n_nodes];
adouble *t = new adouble [n_nodes];
adouble *delta = new adouble [n_nodes - 1];
for (Index i = 0; i < n_nodes; i += 1) {
f[i] = new adouble [n_states];
}
t[0] = *p_t0;
for (Index i = 0; i < n_nodes - 1; i++) {
delta[i] = (*p_tf-*p_t0)*node_str(i+1);
t[i+1] = t[i] + (*p_tf-*p_t0)*node_str(i+1);
}
t[n_nodes - 1] = *p_tf;
if (n_path_constraints)
p_path = &g[path_constraint_idx[0][0]];
p_states0 = &d_z[state_idx[0][0]];
p_controls = &d_z[control_idx[0][0]];
ad_derv(f[0], p_path, p_states0, p_controls, p_parameters, t[0], 1, constants);
if (config.disc_method == Hermite_Simpson) {
adouble* y_m = new adouble [n_states];
adouble* f_m = new adouble [n_states];
for (Index i = 0; i < n_nodes - 1; i += 1) {
if (n_path_constraints)
p_path = &g[path_constraint_idx[2*(i+1)][0]];
p_states1 = &d_z[state_idx[i+1][0]];
p_controls = &d_z[control_idx[2*(i+1)][0]];
ad_derv(f[i+1], p_path, p_states1, p_controls, p_parameters, t[i+1], 1, constants);
for (Index j = 0; j < n_states; j += 1){
y_m[j] = (p_states0[j] + p_states1[j])/2. + delta[i]/8.*(f[i][j]-f[i+1][j]);
}
if (n_path_constraints)
p_path = &g[path_constraint_idx[2*i+1][0]];
p_controls = &d_z[control_idx[2*i+1][0]];
ad_derv(f_m, p_path, y_m, p_controls, p_parameters, (t[i] + t[i+1])/2., 1, constants);
p_defects = &g[defect_idx[i][0]];
for (Index j = 0; j < n_states; j++) {
p_defects[j] = (p_states1[j] - p_states0[j] - delta[i]/6.*(f[i][j]+4.*f_m[j]+f[i+1][j]));
}
p_states0 = p_states1;
}
delete[] y_m;
delete[] f_m;
}
else if (config.disc_method == trapezoidal) {
for (Index i = 0; i < n_nodes - 1; i += 1) {
if (n_path_constraints)
p_path = &g[path_constraint_idx[i+1][0]];
p_states1 = &d_z[state_idx[i+1][0]];
p_controls = &d_z[control_idx[i+1][0]];
ad_derv(f[i+1], p_path, p_states1, p_controls, p_parameters, t[i+1], 1, constants);
p_defects = &g[defect_idx[i][0]];
for (Index j = 0; j < n_states; j++) {
p_defects[j] = p_states1[j] - p_states0[j] - delta[i]/2.0*(f[i][j] + f[i+1][j]);
}
p_states0 = p_states1;
}
}
for (Index i = 0; i < nlp_m; i++) {
g[i] = g[i]/nlp_sf_g[i]+1;
}
for (Index i = 0; i < n_nodes; i++) {
delete[] f[i];
}
delete[] f;
delete[] t;
delete[] delta;
delete[] d_z;
}
#ifdef DCOPT_DEBUG
printf("end of ad_eval_constraints()\n");
#endif
return true;
}
bool MyADOLC_sparseNLP::eval_constraints(Index n, const double *x, Index m, double* g) {
#ifdef DCOPT_DEBUG
printf("eval_constraints()\n");
#endif
double *d_z, *p_controls, *p_states0,*p_states1, *p_states_ini, *p_states_fin, *p_parameters, *p_t0, *p_tf;
double *p_path = NULL, *p_events, *p_defects;
d_z = new double [n];
for( Index i = 0; i < n; i++)
d_z[i] = x[i]*nlp_sf_x[i];
if (n_events)
p_events = &g[event_idx[0]];
else
p_events = NULL;
if (n_nodes != 0 && n_states != 0) {
p_states_ini = &d_z[state_idx[0][0]];
p_states_fin = &d_z[state_idx[n_nodes - 1][0]];
p_t0 = &d_z[t0_idx];
p_tf = &d_z[tf_idx];
}
else{
p_states_ini = NULL;
p_states_fin = NULL;
p_t0 = NULL;
p_tf = NULL;
}
if (n_parameters)
p_parameters = &d_z[parameter_idx[0]];
else
p_parameters = NULL;
d_events(p_events, p_states_ini, p_states_fin, p_parameters, *p_t0, *p_tf, 1, constants);
if(n_states != 0 && n_nodes != 0) {
double **f = new double *[n_nodes];
double *t = new double [n_nodes];
double *delta = new double [n_nodes - 1];
for (Index i = 0; i < n_nodes; i += 1) {
f[i] = new double [n_states];
}
t[0] = *p_t0;
for (Index i = 0; i < n_nodes - 1; i++) {
delta[i] = (*p_tf-*p_t0)*node_str(i+1);
t[i+1] = t[i] + (*p_tf-*p_t0)*node_str(i+1);
}
t[n_nodes - 1] = *p_tf;
if (n_path_constraints)
p_path = &g[path_constraint_idx[0][0]];
p_states0 = &d_z[state_idx[0][0]];
p_controls = &d_z[control_idx[0][0]];
d_derv(f[0], p_path, p_states0, p_controls, p_parameters, t[0], 1, constants);
if (config.disc_method == Hermite_Simpson) {
double* y_m = new double [n_states];
double* f_m = new double [n_states];
for (Index i = 0; i < n_nodes - 1; i += 1) {
if (n_path_constraints)
p_path = &g[path_constraint_idx[2*(i+1)][0]];
p_states1 = &d_z[state_idx[i+1][0]];
p_controls = &d_z[control_idx[2*(i+1)][0]];
d_derv(f[i+1], p_path, p_states1, p_controls, p_parameters, t[i+1], 1, constants);
for (Index j = 0; j < n_states; j += 1){
y_m[j] = (p_states0[j] + p_states1[j])/2. + delta[i]/8.*(f[i][j]-f[i+1][j]);
}
if (n_path_constraints)
p_path = &g[path_constraint_idx[2*i+1][0]];
p_controls = &d_z[control_idx[2*i+1][0]];
d_derv(f_m, p_path, y_m, p_controls, p_parameters, (t[i] + t[i+1])/2., 1, constants);
p_defects = &g[defect_idx[i][0]];
for (Index j = 0; j < n_states; j++) {
p_defects[j] = (p_states1[j] - p_states0[j] - delta[i]/6.*(f[i][j]+4.*f_m[j]+f[i+1][j]));
}
p_states0 = p_states1;
}
delete[] y_m;
delete[] f_m;
}
else if (config.disc_method == trapezoidal) {
for (Index i = 0; i < n_nodes - 1; i += 1) {
if (n_path_constraints)
p_path = &g[path_constraint_idx[i+1][0]];
p_states1 = &d_z[state_idx[i+1][0]];
p_controls = &d_z[control_idx[i+1][0]];
d_derv(f[i+1], p_path, p_states1, p_controls, p_parameters, t[i+1], 1, constants);
p_defects = &g[defect_idx[i][0]];
for (Index j = 0; j < n_states; j++) {
p_defects[j] = p_states1[j] - p_states0[j] - delta[i]/2.0*(f[i][j] + f[i+1][j]);
}
p_states0 = p_states1;
}
}
for (Index i = 0; i < nlp_m; i++) {
g[i] = g[i]/nlp_sf_g[i]+1;
}
for (Index i = 0; i < n_nodes; i++) {
delete[] f[i];
}
delete[] f;
delete[] t;
delete[] delta;
delete[] d_z;
}
#ifdef DCOPT_DEBUG
printf("end of eval_constraints()\n");
#endif
return true;
}
bool MyADOLC_sparseNLP::get_nlp_info(Index& n, Index& m, Index& nnz_jac_g,
Index& nnz_h_lag, IndexStyleEnum& index_style)