/*---------------------------------------*/
void nomad_ ( int * n , // # of variables
int * m , // # of outputs (obj + m-1 constraints)
double * x , // starting point (IN) / solution (OUT)
double * lb , // lower bounds for each variable
double * ub , // upper bounds
int * max_bbe , // max # of evaluations (-1: not considered)
int * display_degree ) { // display_degree (0-4; 0: no display)
// display:
Display out ( std::cout );
out.precision ( DISPLAY_PRECISION_STD );
try {
int i;
// parameters creation:
Parameters p ( out );
p.set_DIMENSION (*n); // number of variables
vector<bb_output_type> bbot (*m); // definition of output types:
bbot[0] = OBJ; // first output : objective value to minimize
for ( i = 1 ; i < *m ; ++i ) // other outputs: constraints cj <= 0
bbot[i] = PB;
p.set_BB_OUTPUT_TYPE ( bbot );
// starting point and bounds:
Point px0 ( *n );
Point plb ( *n );
Point pub ( *n );
for ( i = 0 ; i < *n ; ++i ) {
px0[i] = x [i];
if ( lb[i] > -1e20 )
plb[i] = lb[i];
if ( ub[i] < 1e20 )
pub[i] = ub[i];
}
p.set_X0 ( px0 );
p.set_LOWER_BOUND ( plb );
p.set_UPPER_BOUND ( pub );
p.set_LH_SEARCH ( 50, 0 );
p.set_INITIAL_MESH_SIZE(0.1);
// maximum number of black-box evaluations:
if ( *max_bbe > 0 )
p.set_MAX_BB_EVAL ( *max_bbe );
// display degree:
p.set_DISPLAY_DEGREE ( *display_degree );
// parameters validation:
p.check();
// custom evaluator creation:
My_Evaluator ev ( p );
// algorithm creation and execution:
Mads mads ( p , &ev );
mads.run();
// get the solution:
const Eval_Point * bf = mads.get_best_feasible();
if ( bf )
for ( i = 0 ; i < *n ; ++i )
x[i] = (*bf)[i].value();
}
catch ( exception & e ) {
cerr << "\nNOMAD has been interrupted (" << e.what() << ")\n\n";
}
}
/*------------------------------------------*/
int main ( int argc , char ** argv ) {
// display:
Display out ( std::cout );
out.precision ( DISPLAY_PRECISION_STD );
// NOMAD initializations:
begin ( argc , argv );
try {
// usage:
if ( argc < 2 ) {
display_usage ( cerr );
return EXIT_SUCCESS;
}
// parameters file:
string param_file_name = argv[1];
string opt = param_file_name;
NOMAD::toupper(opt);
// display version if option '-v' has been specified:
if ( opt == "-V" ) {
display_version ( out );
return EXIT_SUCCESS;
}
// display info if option '-i' has been specified:
if ( opt == "-I" || opt == "-INFO" ) {
display_info ( out );
display_usage ( out );
return EXIT_SUCCESS;
}
// parameters creation:
Parameters p ( out );
// display help on parameters if option '-h' has been specified:
if ( opt == "-H" || opt == "-HELP" ) {
p.help( (argc>2) ? argv[2] : "all" );
return EXIT_SUCCESS;
}
// read parameters file:
p.read ( param_file_name );
// parameters check:
p.check();
// display NOMAD info:
if ( p.get_display_degree() > 1 )
display_info ( out );
// parameters display:
if ( p.get_display_degree() > 2 )
out << endl << "parameters:" << endl << p << endl;
// single-objective:
if ( p.get_nb_obj() == 1 ) {
// custom evaluator:
My_Evaluator ev ( p );
// algorithm creation and execution:
Mads mads ( p , &ev );
mads.run();
// plot the last point:
const Eval_Point * bf = mads.get_best_feasible();
if ( bf )
ev.plot_success ( mads.get_stats().get_bb_eval() , bf->get_f() );
}
// bi-objective:
else {
// custom evaluator:
My_Multi_Obj_Evaluator ev ( p );
// algorithm creation and execution:
Mads mads ( p , &ev );
mads.multi_run();
}
}
catch ( exception & e ) {
cerr << "\nNOMAD has been interrupted (" << e.what() << ")\n\n";
}
GUI_wait();
Slave::stop_slaves ( out );
end();
return EXIT_SUCCESS;
}
/*----------------------------------------*/
void Master_Slaves::mads_run ( Cache & cache ) {
const Eval_Point * best_feasible = NULL;
const Eval_Point * best_infeasible = NULL;
Double old_f = INF;
bool stop_algo = false;
int run_index = 0;
int mesh_index = 0;
double default_eps = Double::get_epsilon();
int n = _p.get_dimension();
int * free_vars = new int [_ns];
Point x0 ( n ) , delta_0 , delta_min;
/*---------------------*/
/* main loop */
/*---------------------*/
while ( !stop_algo ) {
best_feasible = NULL;
best_infeasible = NULL;
// random SEED:
_p.set_SEED ( get_pid() + run_index );
// Halton seed:
_p.set_HALTON_SEED ( 7999 + 99 * _rank + 20 * run_index );
// first run:
if ( run_index == 0 ) {
// max number of evaluations for regular slaves:
if ( _rank != 1 )
_p.set_MAX_BB_EVAL ( _bbe );
// display:
// p.set_DISPLAY_STATS ( "process #" + itos(_rank) + " BBE OBJ" );
// p.set_DISPLAY_DEGREE ( FULL_DISPLAY );
_p.set_DISPLAY_DEGREE ( NO_DISPLAY );
}
// force the parameters check:
_p.force_check_flag();
/*------------------*/
/* pollster slave */
/*------------------*/
if ( _rank == 1 ) {
stop_type stop_reason = UNKNOWN_STOP_REASON;
Mesh::get_delta_m ( delta_0 ,
_initial_mesh_size ,
_mesh_update_basis ,
_initial_mesh_index ,
mesh_index );
// we set a very small epsilon in order to accept
// very small initial mesh sizes:
Double::set_epsilon ( 1e-16 );
if ( !check_delta ( delta_0 ) )
stop_algo = true;
else {
// first run:
if ( run_index == 0 ) {
// directions:
{
int halton_seed = _p.get_halton_seed();
bool use_orthomads = _p.has_orthomads_directions();
_p.reset_directions ( halton_seed );
_p.set_DIRECTION_TYPE ( (use_orthomads) ? ORTHO_1 : LT_1 );
}
// cache search:
_p.set_CACHE_SEARCH ( true );
_p.set_OPPORTUNISTIC_CACHE_SEARCH ( false );
}
// other runs:
else {
// stop_algo may be set to 'false' here:
receive_optimization_data ( stop_algo , x0 , old_f );
// starting point:
_p.reset_X0();
_p.set_X0 ( x0 );
}
if ( !stop_algo ) {
// pollster mesh:
_p.set_INITIAL_MESH_INDEX ( mesh_index );
_p.set_MAX_MESH_INDEX ( mesh_index );
_p.set_INITIAL_MESH_SIZE ( delta_0 );
_p.set_MIN_MESH_SIZE ( delta_0 );
// check the parameters:
_p.check();
Double::set_epsilon ( default_eps );
Mads mads ( _p , NULL , NULL , &cache , NULL );
stop_reason = mads.run();
best_feasible = mads.get_best_feasible();
best_infeasible = mads.get_best_infeasible();
bool success = false;
if ( best_feasible ) {
success = (best_feasible->get_f() < old_f);
if ( _debug )
_p.out() << "POLLSTER: ELL="
<< mesh_index << " BBE="
<< mads.get_stats().get_bb_eval()
<< " OLD_F=" << old_f << " NEW_F="
<< best_feasible->get_f()
<< " SUCCESS=" << success << endl;
}
// pollster mesh update:
if ( success )
--mesh_index;
else
++mesh_index;
}
}
send_optimization_result ( mesh_index ,
stop_algo ,
best_feasible ,
best_infeasible ,
stop_reason );
}
/*------------------*/
/* regular slaves */
/*------------------*/
else {
int i , j , pollster_mesh_index;
receive_optimization_data ( stop_algo ,
x0 ,
old_f ,
pollster_mesh_index ,
free_vars );
if ( _debug ) {
_p.out() << "SLAVE #" << _rank
<< ": OPTIM. DATA: [STOP=" << stop_algo
<< "] [POLLSTER_MESH_INDEX=" << pollster_mesh_index
<< "] [X0=" << x0 << " ] [f(X0)="
<< old_f << "] [FREE VARS= ";
for ( i = 0 ; i < _ns ; ++i )
_p.out() << free_vars[i] << " ";
_p.out() << " ]" << endl;
}
if ( !stop_algo ) {
// starting point:
_p.reset_X0();
_p.set_X0 ( x0 );
// mesh of the regular slave:
int ell_0 = 0;
if ( pollster_mesh_index < ell_0 )
ell_0 = pollster_mesh_index;
Mesh::get_delta_m ( delta_0 ,
_initial_mesh_size ,
_mesh_update_basis ,
_initial_mesh_index ,
ell_0 );
Mesh::get_delta_m ( delta_min ,
_initial_mesh_size ,
_mesh_update_basis ,
_initial_mesh_index ,
pollster_mesh_index );
Double::set_epsilon ( 1e-16 );
if ( !check_delta ( delta_0 ) || !check_delta ( delta_min ) )
stop_algo = true;
else {
// mesh of the regular slave:
_p.set_INITIAL_MESH_INDEX ( ell_0 );
_p.set_MAX_MESH_INDEX ( pollster_mesh_index );
_p.set_INITIAL_MESH_SIZE ( delta_0 );
_p.set_MIN_MESH_SIZE ( delta_min );
// free variables:
{
_p.reset_fixed_variables();
bool fix_var;
for ( i = 0 ; i < n ; ++i ) {
fix_var = true;
for ( j = 0 ; j < _ns ; ++j )
if ( free_vars[j] == i ) {
fix_var = false;
break;
}
if ( fix_var )
_p.set_FIXED_VARIABLE ( i );
}
}
// check the parameters:
_p.check();
Double::set_epsilon ( default_eps );
// MADS run:
Mads mads ( _p , NULL , NULL , &cache , NULL );
mads.run();
best_feasible = mads.get_best_feasible();
best_infeasible = mads.get_best_infeasible();
if ( _debug && best_feasible ) {
_p.out() << "RANK #" << _rank << ": POLLSTER_ELL="
<< pollster_mesh_index << " VARS = [";
for ( i = 0 ; i < _ns ; ++i )
_p.out() << free_vars[i] << " ";
_p.out() << " ] BBE=" << mads.get_stats().get_bb_eval()
<< " OLD_F=" << old_f << " NEW_F="
<< best_feasible->get_f()
<< " SUCCESS="
<< (best_feasible->get_f() < old_f)
<< endl;
}
}
}
{
int tmp1 = -1;
bool tmp2 = false;
stop_type tmp3 = UNKNOWN_STOP_REASON;
send_optimization_result ( tmp1 ,
tmp2 ,
best_feasible ,
best_infeasible ,
tmp3 );
}
}
// loop increment:
++run_index;
}
delete [] free_vars;
}
/*------------------------------------------*/
int main ( int argc , char ** argv ) {
// display:
Display out ( std::cout );
out.precision ( DISPLAY_PRECISION_STD );
// NOMAD initializations:
begin ( argc , argv );
try {
// usage:
if ( argc != 4 ) {
cerr << "\nusage: multi param.txt nb_circles nb_mads_runs\n\n";
return 1;
}
int NBC = atoi(argv[2] );
if ( NBC < 4 ) {
cerr << "\nthe number of circles must be > 3\n\n";
return 1;
}
int N = 2*NBC-3;
//int M = ( NBC * ( NBC + 1 ) - 8 ) / 2;
int M = 2;
// list of x0 points (LH strategy is used):
// -----------------
vector<Point *> x0_pts;
// srand ( static_cast<int> ( time(0) ) );
srand(0);
rand();
int i;
int nb_mads_runs = atoi ( argv[3] );
LH_x0 ( N , nb_mads_runs , x0_pts );
// read best_x.txt:
ifstream fin ( "best_x.txt");
if ( !fin.fail() )
for ( int i = 0 ; i < N ; ++i )
fin >> (*x0_pts[0])[i];
fin.close();
// display all starting points:
out << endl;
for ( int j = 0 ; j < nb_mads_runs ; ++j )
out << "starting point # " << j << ": ( " << *x0_pts[j] << " )" << endl;
out << endl;
// parameters creation:
// --------------------
Parameters param ( out );
param.set_DIMENSION (N);
vector<bb_output_type> bbot (M+1);
for ( i = 0 ; i < M ; ++i )
bbot[i] = PB;
bbot[M] = OBJ;
param.set_BB_OUTPUT_TYPE ( bbot );
Point lb ( N , LB );
Point ub ( N , UB );
param.set_LOWER_BOUND ( lb );
param.set_UPPER_BOUND ( ub );
param.read ( argv[1] );
param.set_DISPLAY_DEGREE (0);
param.set_X0 ( *x0_pts[0] );
// parameters check:
param.check();
// out << param << endl;
// custom evaluator creation:
My_Evaluator ev ( param , NBC , M );
const Eval_Point * cur_x;
Point best_x (N);
Double best_f = INF , worst_f = 0.0 , avg_f = 0.0;
// MADS runs:
// ----------
int bbe = 0;
i = 0;
while ( true ) {
// algorithm creation:
Mads mads ( param , &ev );
mads.run();
bbe += mads.get_cache().size();
// displays and remember the best point:
out << "run #" << setw(2) << i << ": ";
cur_x = mads.get_best_feasible();
if ( cur_x ) {
out << "f=" << cur_x->get_f() << endl;
if ( cur_x->get_f() < best_f ) {
best_f = cur_x->get_f();
best_x = *cur_x;
}
if ( cur_x->get_f() > worst_f )
worst_f = cur_x->get_f();
avg_f += cur_x->get_f();
}
else
out << "NULL" << endl;
if ( ++i == nb_mads_runs )
break;
// preparation of next run:
mads.reset();
param.reset_X0();
param.set_X0 ( *x0_pts[i] );
param.check();
}
// display the solution:
out << endl << "bb eval : " << bbe << endl
<< "best : " << best_f;
out << endl
<< "worst : " << worst_f << endl
<< "solution: ";
out << "x = ( ";
best_x.display ( out , " " , -1 , -1 );
out << " ) f(x) = " << best_f.value();
out << endl << endl;
ofstream fout ( "best_x.txt" );
fout << setprecision(32);
best_x.display ( fout , " " , -1 , -1 );
fout.close();
// delete x0 points:
for ( i = 0 ; i < nb_mads_runs ; ++i )
delete x0_pts[i];
}
catch ( exception & e ) {
cerr << "\nNOMAD has been interrupted (" << e.what() << ")\n\n";
}
end();
return EXIT_SUCCESS;
}