int CGSolver(SparseMatrix A, // CSR format matrix
const std::vector<double> &b,
std::vector<double> &u,
double tol,
std::map<int, std::vector<double>> &soln_iter) {
unsigned int n_iter = 0;
long unsigned int max_iter = A.GetRows();
soln_iter[n_iter] = u; // store initial state
auto A_u = A.MulVec(u); // A*u
std::vector<double> r = subvec(b, A_u); // b - A*u
auto L2normr0 = L2norm(r);
std::vector<double> p = r; // p =r
while (n_iter < max_iter) {
n_iter++;
auto A_p = A.MulVec(p); // A * p
double alpha = vecvec(r, r) / vecvec(p, A_p); // (r * r)/(p * A * p)
auto alpha_p = scalvec(alpha, p); // alpha * p
u = addvec(u, alpha_p); // u = u + alpha * p
/**
* alpha * A * p
* r_n+1 = r_n - alpha * A * p
*/
std::vector<double> alpha_A_p = scalvec(alpha, A_p);
std::vector<double> r_next = subvec(r, alpha_A_p);
auto L2normr = L2norm(r_next); // L2normr = L2norm(r_n+1)
if (L2normr / L2normr0 < tol) {
soln_iter[n_iter] = u;
std::cout << "SUCCESS: CG solver converged in "
<< n_iter << " iterations" << std::endl;
break;
} else {
double beta = vecvec(r_next, r_next) / vecvec(r, r);
auto beta_p = scalvec(beta, p);
p = addvec(r_next, beta_p);
r = r_next;
if ((n_iter % 10 == 0) || (n_iter == 0)) {
soln_iter[n_iter] = u;
}
if (n_iter == max_iter) {
std::cout << "FAILURE: CG solver failed to converge" << std::endl;
return 1;
}
}
}
return 0;
}
/*
data: N data instance
assignment: N by 1 vector indicating assignment of one atom
*/
void compute_means (vector<Instance*>& data, vector<int>& assignment, int FIX_DIM, vector< vector<double> >& means) {
int N = data.size();
assert (assignment.size() == N);
int nClusters = means.size(); // for 0-index
vector< vector<int> > group_points (nClusters, vector<int>());
for (int i = 0; i < N; i ++)
group_points[assignment[i]].push_back(i);
for (int c = 0; c < nClusters; c++) {
int num_points = group_points[c].size();
if (num_points == 0) {
std::fill(means[c].begin(), means[c].end(), -INF);
continue;
}
std::fill(means[c].begin(), means[c].end(), 0.0);
double ** local_dist_mat = mat_init(num_points, num_points);
for (int x = 0; x < num_points; x++) {
for (int y = 0; y < num_points; y++) {
Instance* a = data[group_points[c][x]];
Instance* b = data[group_points[c][y]];
double dist = L2norm(a, b, FIX_DIM);
local_dist_mat[x][y] = dist * dist;
}
}
double* sum_dist = new double [num_points];
mat_sum_col(local_dist_mat, sum_dist, num_points, num_points);
int min_index = -1;
double min_value = INF;
for (int j = 0; j < num_points; j++)
if (sum_dist[j] < min_value) {
min_index = j;
min_value = sum_dist[j];
}
assert (min_index < num_points);
Instance* new_medoid = data[group_points[c][min_index]];
for (int f = 0; f < new_medoid->fea.size(); f ++)
means[c][new_medoid->fea[f].first-1] = new_medoid->fea[f].second ;
delete [] sum_dist;
mat_free (local_dist_mat, num_points, num_points);
}
}
YtypeScalar norm(grid2D<Ytype,YtypeScalar,Xtype > & rhs)
{
return L2norm(rhs);
}
int main()
{
std_setup();
int n = 256;
double x0 = 10;
double tf = 10.0;
double dt = 0.05;
int expansion_order = 7;
int hdaf_order = 8;
grid2D<double,double,double > grid( n,-10,10.0, n,-10,10.0 ),u,v,C2,damping;
grid = 0.0;
u = grid;
v = grid;
C2 = grid;
damping = grid;
u = u_0_func();
C2 = C2_func();
damping = damping_func();
sprintf(fname,"/workspace/output/acoustic_propagate_2d/out_dat/C2.dat" );
writeFile(C2,fname);
void * pdata = 0;
method1_init( &pdata, grid.n1, grid.n2, grid.dx1(), grid.dx2(), C2.array, damping.array, 7, hdaf_order, hdaf_order, 0.8, 0.8 );
//acoustic_propagator P;
//P.init( grid, 7, C2, damping );
double t = 0.0;
cout << "step: " << t << "\t" << L2norm(u) << endl;
n=0;
double * b_times = new double [ int(tf/dt+5) ];
while (t <= tf)
{
output(t,u,v);
double t1 = get_real_time();
method1_execute( pdata, dt, u.array,v.array,u.array,v.array );
//P( dt, u,v,u,v );
double t2 = get_real_time();
b_times[n] = t2-t1;
double mean = 0;
for (int j=0; j<n; j++)
mean += b_times[j]/n;
cout << mean << endl;
n++;
double mag = L2norm(u);
cout << "step: " << t << "\t" << t2-t1 << "\t" << mag << endl;
t += dt;
if ( mag > 1E10 ) break;
}
output(t,u,v);
}
