bool placement_problem::is_solution_correct(std::vector<point> const pos) const{
/*
for(int i=0; i<cells.size(); ++i){
if(pos[i].x % cells[i].x_pitch != 0) return false;
if(pos[i].y % cells[i].y_pitch != 0) return false;
}
*/
/*for(int i=0; i<cells.size(); ++i){
rect const & cur = position_constraints[i];
if(cur.xmin > cur.xmax or cur.xmin > pos[i].x or cur.xmax < pos[i].x) return false;
if(cur.ymin > cur.ymax or cur.ymin > pos[i].y or cur.ymax < pos[i].y) return false;
}*/
if(not is_feasible()) return false;
for(rect const R : fixed_elts){
for(int i=0; i<cells.size(); ++i){
if(pos[i].x + cells[i].width > R.xmin
and pos[i].y + cells[i].height > R.ymin
and R.xmax > pos[i].x
and R.ymax > pos[i].y) return false;
}
}
// TODO: Should use a line sweep to verify that there is no overlap
for(int i=0; i+1<cells.size(); ++i){
for(int j=i+1; j<cells.size(); ++j){
if(pos[i].x + cells[i].width > pos[j].x
and pos[j].x + cells[j].width > pos[i].x
and pos[i].y + cells[i].height > pos[j].y
and pos[j].y + cells[j].height > pos[i].y) return false;
}
}
return true;
}
/* print all 0-1 solutions to the lp into the outstream */
void print_bin_solutions_lp(LinearProgram* lp) {
num_t* configuration = allocate(lp->cols, sizeof(*configuration));
unsigned long count = 1UL << lp->cols;
unsigned int feasible_solutions = 0;
print_matrix(lp);
printf("\n");
clock_t start = clock();
unsigned int i;
for (i = 0; i < count; i++) {
if (is_feasible(configuration, lp)) {
__print_config(configuration, lp->cols);
feasible_solutions++;
}
next_configuration(configuration, lp->cols);
}
double elapsed = GET_SEC(start, clock());
printf("Checked %lu vectors in %.3f s = %.3f kvecs/s\n",
count, elapsed, (double) count / elapsed / 1000.0);
deallocate(configuration);
printf("found %u feasible solutions\n", feasible_solutions);
}
/* returns TRUE if the vector in values is a feasible solution to the lp */
long __declspec(dllexport) WINAPI _is_feasible(lprec *lp, double *values)
{
long ret;
if (lp != NULL) {
freebuferror();
ret = is_feasible(lp, values);
}
else
ret = FALSE;
return(ret);
}
void n_queens_helper (int n, int k,
std::vector<std::vector<int>>& res,
std::vector<int>& rows) {
if (n == k) {
res.emplace_back(rows);
return;
}
for (int i = 0; i < n; ++i) {
if (is_feasible(i, k, rows)) {
rows[k] = i;
n_queens_helper(n, k+1, res, rows);
rows[k] = -1;
}
}
}
/* the optimization loop */
int main(int argc, char **argv) {
cmaes_t evo; /* an CMA-ES type struct or "object" */
double *arFunvals, *xfinal, *const*pop;
int i,j;
int numberDipoles;
int id; //Rank
int p; //Number processors
double elapsed_time;//Time from beginning.
double bestValue;
int lambda;
int maxLambda;
int * sendCnts; //For MPI_Alltoallv for arFunVals
int * sdispls; //For MPI_Alltoallv for arFunVals
int * recvCnts; //For MPI_Alltoallv for arFunVals
int * rdispls; //For MPI_Alltoallv for arFunVals
int * sendCntsPop; //For MPI_Alltoallv for pop
int * sdisplsPop; //For MPI_Alltoallv for pop
int * recvCntsPop; //For MPI_Alltoallv for pop
int * rdisplsPop; //For MPI_Alltoallv for pop
int canTerminate;
int canTerminateBuffer;
//Start MPI
MPI_Init(&argc, &argv);
MPI_Barrier(MPI_COMM_WORLD);
elapsed_time = -MPI_Wtime(); //Set initial time.
MPI_Comm_rank(MPI_COMM_WORLD, &id); //Set id
MPI_Comm_size(MPI_COMM_WORLD, &p); //set p
for (i=0;i<32;i++)
{
observations[i]/=1000.0;
}
//Set number of dipoles, either first argument or default value of 2.
numberDipoles=2;
if (argc>=2)
{
numberDipoles=atoi(argv[1]);
}
//Set lambda based on entry, default of 40
maxLambda=40;
if (argc>=3)
{
maxLambda=atoi(argv[2]);
}
if (id==0)
{
printf("Dipoles:%d MaxLambda:%d\n",numberDipoles,maxLambda);
}
//Allocate lambda pieces to each processor, based on the size of maxLambda and the number of processors.
lambda = BLOCK_SIZE(id,p,maxLambda);
printf("Id:%d Lambda:%d\n",id,lambda);
//Setup send and receive buffers for function evaluations and populations that resulted in those evaluation.
sendCnts = malloc(p*sizeof(int));
sdispls = malloc(p*sizeof(int));
recvCnts = malloc(p*sizeof(int));
rdispls = malloc(p*sizeof(int));
sendCntsPop = malloc(p*sizeof(int));
sdisplsPop = malloc(p*sizeof(int));
recvCntsPop = malloc(p*sizeof(int));
rdisplsPop = malloc(p*sizeof(int));
for (i=0;i<p;i++)
{
sendCnts[i]=lambda;//Same for all others
sdispls[i] = BLOCK_LOW(id,p,maxLambda);//Same for all others
recvCnts[i] = BLOCK_SIZE(i,p,maxLambda);//Depends on which we receive from.
rdispls[i] = BLOCK_LOW(i,p,maxLambda);
sendCntsPop[i]=lambda*((numberDipoles*6+2));//Same for all others
sdisplsPop[i] = BLOCK_LOW(id,p,maxLambda)*(numberDipoles*6+2);//Same for all others
recvCntsPop[i] = BLOCK_SIZE(i,p,maxLambda)*(numberDipoles*6+2);//Depends on which we receive from.
rdisplsPop[i] = BLOCK_LOW(i,p,maxLambda)*(numberDipoles*6+2);
}
for (i=0;i<p;i++)
{
printf("Id: %d recvCnts[%d]=%d\n",id,i,recvCnts[i]);
printf("Id: %d rdispls[%d]=%d\n",id,i,rdispls[i]);
printf("Id: %d recvCntsPop[%d]=%d\n",id,i,recvCntsPop[i]);
printf("Id: %d rdisplsPop[%d]=%d\n",id,i,rdisplsPop[i]);
}
/* Initialize everything into the struct evo, 0 means default */
//arFunvals = cmaes_init(&evo, 0, NULL, NULL, 0, 0, "initials.par");
// printf("0\n");
//The maxLambda parameter has been added so all of them will have enough space to store the results
arFunvals = reinit(&evo, maxLambda, numberDipoles);
//outputCMAES_t(evo,1);
// printf("1\n");
resetSignals(&evo, numberDipoles); /* write header and initial values */
//Reset the seed value based on processor (so they don't all come out the same!
evo.sp.seed=evo.sp.seed*(id+1)/p;
printf("proc: %d seed: %d\n",id,evo.sp.seed);
//outputCMAES_t(evo,0);
// printf("2\n");
// printf("%s\n", cmaes_SayHello(&evo));
// i=40;
// for (i=32;i<40;i*=2)
// {
// arFunvals = reinit(&evo, i);
//outputCMAES_t(evo);
evo.sp.lambda=lambda;
canTerminate = (0==1);
/* Iterate until stop criterion holds */
while(!canTerminate)
{
/* generate lambda new search points, sample population */
pop = cmaes_SamplePopulation(&evo); /* do not change content of pop */
/* Here you may resample each solution point pop[i] until it
becomes feasible, e.g. for box constraints (variable
boundaries). function is_feasible(...) needs to be
user-defined.
Assumptions: the feasible domain is convex, the optimum is
not on (or very close to) the domain boundary, initialX is
feasible and initialStandardDeviations are sufficiently small
to prevent quasi-infinite looping.
*/
/*for (i = 0; i < lambda; ++i)
{
cmaes_ReSampleSingle(&evo, i);
}*/
for (i = 0; i < lambda; ++i)
{
while (!is_feasible(evo.rgrgx[i],(int) cmaes_Get(&evo, "dim")))
{
cmaes_ReSampleSingle(&evo, i);
}
}
for (i=0;i<lambda;i++)
{
for(j=0;j<(6*numberDipoles)+2;j++)
{
evo.rgrgx[BLOCK_LOW(id,p,maxLambda)+i][j]=evo.rgrgx[i][j];
}
}
/* evaluate the new search points using fitfun from above */
for (i = BLOCK_LOW(id,p,maxLambda); i <= BLOCK_HIGH(id,p,maxLambda); ++i) {
arFunvals[i] = fitfun(evo.rgrgx[i], (int) cmaes_Get(&evo, "dim"));
//printf("ID:%d, arFunvals[%d]=%lf\n",id,i,arFunvals[i]);
}
//Now communicate the arFunvals around
MPI_Alltoallv(arFunvals,sendCnts,sdispls,MPI_DOUBLE,arFunvals,recvCnts,rdispls,MPI_DOUBLE,MPI_COMM_WORLD);
//Now communicate the populations being looked at around
MPI_Alltoallv(&evo.rgrgx[0][0],sendCntsPop,sdisplsPop,MPI_DOUBLE,&evo.rgrgx[0][0],recvCntsPop,rdisplsPop,MPI_DOUBLE,MPI_COMM_WORLD);
/* update the search distribution used for cmaes_SampleDistribution() */
cmaes_UpdateDistribution(&evo, arFunvals);
//Test for any that can terminate.
canTerminate = cmaes_TestForTermination(&evo);
if (canTerminate)
{
printf("id:%d can terminate for reason:%s\n",id,cmaes_TestForTermination(&evo));
}
MPI_Allreduce(&canTerminate,&canTerminateBuffer,1,MPI_INT,MPI_MAX,MPI_COMM_WORLD);//Get the max, if any are >0, then someone has terminated.
canTerminate = canTerminateBuffer;//Reset so the loop will exit.
/* read instructions for printing output or changing termination conditions */
// cmaes_ReadSignals(&evo, "signals.par");
// fflush(stdout); /* useful in MinGW */
}
// printf("Stop:\n%s\n", cmaes_TestForTermination(&evo)); /* print termination reason */
// cmaes_WriteToFile(&evo, "all", "allcmaes.dat"); /* write final results */
elapsed_time += MPI_Wtime();
/* get best estimator for the optimum, xmean */
xfinal = cmaes_GetNew(&evo, "xmean"); /* "xbestever" might be used as well */
bestValue = fitfun(xfinal, (int) cmaes_Get(&evo, "dim"));
printf("Proccesor:%d has last mean of:%lf elapsedTime:%lf\n",id,bestValue,elapsed_time);
for (i=0;i<6*numberDipoles;i++)
{
printf("(%d:%d:%lf)\n",id,i,xfinal[i]);
}
// cmaes_exit(&evo); /* release memory */
/* do something with final solution and finally release memory */
free(xfinal);
free(sendCnts);
free(sdispls);
free(recvCnts);
free(rdispls);
free(sendCntsPop);
free(sdisplsPop);
free(recvCntsPop);
free(rdisplsPop);
MPI_Finalize();
//}
return 0;
}
/* the optimization loop */
int main(int argn, char **args) {
cmaes_t evo; /* an CMA-ES type struct or "object" */
boundary_transformation_t boundaries;
double *arFunvals, *x_in_bounds, *const*pop;
double lowerBounds[] = {1.0, DBL_MAX / -1e2}; /* last number is recycled for all remaining coordinates */
double upperBounds[] = {3, 2e22};
int nb_bounds = 1; /* numbers used from lower and upperBounds */
unsigned long dimension;
int i;
/* initialize boundaries, be sure that initialSigma is smaller than upper minus lower bound */
boundary_transformation_init(&boundaries, lowerBounds, upperBounds, nb_bounds);
/* Initialize everything into the struct evo, 0 means default */
arFunvals = cmaes_init(&evo, 0, NULL, NULL, 0, 0, "cmaes_initials.par");
dimension = (unsigned long)cmaes_Get(&evo, "dimension");
printf("%s\n", cmaes_SayHello(&evo));
x_in_bounds = cmaes_NewDouble(dimension); /* calloc another vector */
cmaes_ReadSignals(&evo, "cmaes_signals.par"); /* write header and initial values */
/* Iterate until stop criterion holds */
while(!cmaes_TestForTermination(&evo))
{
/* generate lambda new search points, sample population */
pop = cmaes_SamplePopulation(&evo); /* do not change content of pop */
/* transform into bounds and evaluate the new search points */
for (i = 0; i < cmaes_Get(&evo, "lambda"); ++i) {
boundary_transformation(&boundaries, pop[i], x_in_bounds, dimension);
/* this loop can be omitted if is_feasible is invariably true */
while(!is_feasible(x_in_bounds, dimension)) { /* is_feasible needs to be user-defined, in case, and can change/repair x */
cmaes_ReSampleSingle(&evo, i);
boundary_transformation(&boundaries, pop[i], x_in_bounds, dimension);
}
arFunvals[i] = fitfun(x_in_bounds, dimension); /* evaluate */
}
/* update the search distribution used for cmaes_SampleDistribution() */
cmaes_UpdateDistribution(&evo, arFunvals); /* assumes that pop[i] has not been modified */
/* read instructions for printing output or changing termination conditions */
cmaes_ReadSignals(&evo, "cmaes_signals.par");
fflush(stdout); /* useful in MinGW */
}
printf("Stop:\n%s\n", cmaes_TestForTermination(&evo)); /* print termination reason */
cmaes_WriteToFile(&evo, "all", "allcmaes.dat"); /* write final results */
/* get best estimator for the optimum, xmean */
boundary_transformation(&boundaries,
(double const *) cmaes_GetPtr(&evo, "xmean"), /* "xbestever" might be used as well */
x_in_bounds, dimension);
/* do something with final solution x_in_bounds */
/* ... */
/* and finally release memory */
cmaes_exit(&evo); /* release memory */
boundary_transformation_exit(&boundaries); /* release memory */
free(x_in_bounds);
return 0;
}
path_searcht::resultt path_searcht::operator()(
const goto_functionst &goto_functions)
{
locst locs(ns);
var_mapt var_map(ns);
locs.build(goto_functions);
// this is the container for the history-forest
path_symex_historyt history;
queue.push_back(initial_state(var_map, locs, history));
// set up the statistics
number_of_dropped_states=0;
number_of_paths=0;
number_of_VCCs=0;
number_of_steps=0;
number_of_feasible_paths=0;
number_of_infeasible_paths=0;
number_of_VCCs_after_simplification=0;
number_of_failed_properties=0;
number_of_locs=locs.size();
// stop the time
start_time=current_time();
initialize_property_map(goto_functions);
while(!queue.empty())
{
number_of_steps++;
// Pick a state from the queue,
// according to some heuristic.
// The state moves to the head of the queue.
pick_state();
// move into temporary queue
queuet tmp_queue;
tmp_queue.splice(
tmp_queue.begin(), queue, queue.begin(), ++queue.begin());
try
{
statet &state=tmp_queue.front();
// record we have seen it
loc_data[state.get_pc().loc_number].visited=true;
debug() << "Loc: #" << state.get_pc().loc_number
<< ", queue: " << queue.size()
<< ", depth: " << state.get_depth();
for(const auto & s : queue)
debug() << ' ' << s.get_depth();
debug() << eom;
if(drop_state(state))
{
number_of_dropped_states++;
number_of_paths++;
continue;
}
if(!state.is_executable())
{
number_of_paths++;
continue;
}
if(eager_infeasibility &&
state.last_was_branch() &&
!is_feasible(state))
{
number_of_infeasible_paths++;
number_of_paths++;
continue;
}
if(number_of_steps%1000==0)
{
status() << "Queue " << queue.size()
<< " thread " << state.get_current_thread()
<< '/' << state.threads.size()
<< " PC " << state.pc() << messaget::eom;
}
// an error, possibly?
if(state.get_instruction()->is_assert())
{
if(show_vcc)
do_show_vcc(state);
else
{
check_assertion(state);
// all assertions failed?
if(number_of_failed_properties==property_map.size())
break;
}
}
// execute
path_symex(state, tmp_queue);
// put at head of main queue
queue.splice(queue.begin(), tmp_queue);
}
catch(const std::string &e)
{
error() << e << eom;
number_of_dropped_states++;
}
catch(const char *e)
{
error() << e << eom;
number_of_dropped_states++;
}
catch(int)
{
number_of_dropped_states++;
}
}
report_statistics();
return number_of_failed_properties==0?SAFE:UNSAFE;
}
// Main algorithm.
void MOGA::compute()
{
if ( num_bits_ == 0 ) return;
double p;
// Build initial population.
initialization();
// Do generations.
for ( int i = 0; i < num_generations_; ++i )
{
while ( (int)population_.size() < 2 * num_individuals_ )
{
// Select a pair of individuals.
std::pair<int, int> s( selection() );
p = (double)std::rand() / (double)RAND_MAX;
if ( p <= Pc_ )
{
// Cross them.
std::pair<individual, individual> children(
crossover( population_[s.first], population_[s.second] ) );
p = (double)std::rand() / (double)RAND_MAX;
if ( p <= Pm_ )
{
// Mutate children.
mutation( children.first );
mutation( children.second );
}
// Repair children.
repair( children.first );
repair( children.second );
population_.push_back( children.first );
population_.push_back( children.second );
}
else
{
population_.push_back( population_[s.first] );
population_.push_back( population_[s.second] );
}
}
// Keep the best individuals.
elitism();
print();
}
// LOCAL SEARCH
solutions_.clear();
if ( Argument::local_search )
{
LocalSearch local( data_, cust_, fac_ );
local.pipe_fp_ = pipe_fp_;
for ( unsigned int i = 0; i < population_.size(); ++i )
{
local.search( population_[i] );
solutions_.merge( local.solutions_ );
}
}
// FINISHED - KEEP BEST VALUES
// Keep only "first rank" solutions.
for ( unsigned int i = 0; i < population_.size(); ++i )
{
#if STRICT_CHECK
if ( population_[i].rank == 1 && is_feasible( population_[i] ) && population_[i].is_feasible() )
#else
if ( population_[i].rank == 1 && population_[i].is_feasible() )
#endif
{
#if STRICT_CHECK
// Check objective
is_valid( population_[i] );
#endif
Solution sol( getNbObjective() );
for ( int k = 0; k < getNbObjective(); ++k )
{
sol.setObj( k, population_[i].obj[k] );
}
solutions_.push_back( sol );
}
}
}
int placement_problem::get_cost() const{
int ret = x_flow.get_cost() + y_flow.get_cost();
if(is_feasible())
assert(get_solution_cost(get_positions()) == ret);
return ret;
}
bool placement_problem::operator<(placement_problem const & o) const{
if(is_feasible() and o.is_feasible()) return get_cost() < o.get_cost();
else return (not is_feasible()) and o.is_feasible(); // Unfeasible first
}
