void FB_CTUD_DINT::executeEvent(int pa_nEIID) {
if (pa_nEIID == scm_nEventREQID) {
if (true == R()) {
CV() = 0;
}
else {
if (true == LD()) {
CV() = PV();
}
else {
if (!(CU() && CD())) {
if ((CU() && (CV() < CIEC_DINT::scm_nMaxVal))) {
CV() = CV() + 1;
}
else {
if ((CD() && (CV() > CIEC_DINT::scm_nMinVal))) {
CV() = CV() - 1;
}
}
}
}
}
QU() = (CV() >= PV());
QD() = (CV() <= 0);
sendOutputEvent(scm_nEventCNFID);
}
}
void FB_CTUD::executeEvent(AppBlocInfo pa_stAppBlocInfo, EVENT_UID pa_unEIID) {
if (m_unEventREQID == pa_unEIID) {
if (true == R()) {
CV() = 0;
}
else {
if (true == LD()) {
CV() = PV();
}
else {
if (!(CU() && CD())) {
if ((CU() && (CV() < CIEC_DT_INT::scm_nMaxVal))) {
CV() = static_cast<FzrteInt16>(CV() + 1);
}
else {
if ((CD() && (CV() > CIEC_DT_INT::scm_nMinVal))) {
CV() = static_cast<FzrteInt16>(CV() - 1);
}
}
}
}
}
QU() = (CV() >= PV());
QD() = (CV() <= 0);
SendOutput(m_unEventCNFID);
}
}
//------------------------------------------------------------------------------
void MfLowRankApproximation::initialize()
{
double dx;
double constValue;
double endValue;
double constEnd;
double epsilon;
dx = grid.DX;
try {
nOrbitals = cfg->lookup("spatialDiscretization.nSpatialOrbitals");
constEnd = cfg->lookup("meanFieldIntegrator.lowRankApproximation.constEnd");
constValue = cfg->lookup("meanFieldIntegrator.lowRankApproximation.constValue");
endValue = cfg->lookup("meanFieldIntegrator.lowRankApproximation.endValue");
epsilon = cfg->lookup("meanFieldIntegrator.lowRankApproximation.epsilon");
} catch (const SettingNotFoundException &nfex) {
cerr << "MfLowRankApproximation::Error reading entry from config object." << endl;
exit(EXIT_FAILURE);
}
int nConst = constEnd/dx;
int constCenter = nGrid/2;
Vxy = zeros(nGrid, nGrid);
for(uint p=0; p<potential.size(); p++) {
for(int i=0; i<nGrid; i++) {
for(int j=0; j<nGrid; j++) {
Vxy(i, j) += potential[p]->evaluate(i, j);
}
}
}
// Using a simple discretization equal to the discretization of
// the system.
mat h = hExactSpatial();
// mat h = hPiecewiseLinear();
mat Q = eye(nGrid,nGrid);
// Using a consant weight in the center of the potential
// and a linear decrease from the center.
vec g = gLinear(nGrid, constCenter, nConst, constValue, endValue);
mat C = cMatrix(g, h, dx);
vec lambda;
mat eigvec;
eig_sym(lambda, eigvec, inv(C.t())*Vxy*inv(C));
mat Ut = C.t()*eigvec;
mat QU = inv(Q)*Ut;
// Sorting the eigenvalues by absoulte value and finding the number
// of eigenvalues with abs(eigenval(i)) > epsilon
uvec indices = sort_index(abs(lambda), 1);
M = -1;
for(uint m=0; m <lambda.n_rows; m++) {
cout << abs(lambda(indices(m))) << endl;
if(abs(lambda(indices(m))) < epsilon) {
M = m;
break;
}
}
// cout << min(abs(lambda)) << endl;
// cout << "hei" << endl;
// cout << lambda << endl;
// M = 63;
if(M < 0) {
cerr << "MfLowRankApproximation:: no eigenvalues < epsilon found."
<< " Try setting epsilon to a higher number" << endl;
exit(EXIT_FAILURE);
}
eigenval = zeros(M);
for(int m=0; m <M; m++) {
eigenval(m) = lambda(indices(m));
}
// Calculating the U matrix
U = zeros(nGrid, M);
for(int m=0; m < M; m++) {
for(uint j=0; j<h.n_rows; j++) {
U(j,m) = 0;
for(uint i=0; i<h.n_cols; i++) {
U(j,m) += h(j,i)*QU(i,indices(m));
}
}
}
cout << "MfLowRankApproximation:: Trunction of eigenvalues at M = " << M << endl;
#if 1 // For testing the low rank approximation's accuracy
mat appV = zeros(nGrid, nGrid);
for(int i=0; i<nGrid; i++) {
for(int j=0; j<nGrid; j++) {
appV(i,j) = 0;
for(int m=0; m<M; m++) {
appV(i,j) += eigenval(m)*U(i,m)*U(j,m);
}
}
}
mat diffV = abs(Vxy - appV);
cout << "max_err = " << max(max(abs(Vxy - appV))) << endl;
diffV.save("../DATA/diffV.mat");
appV.save("../DATA/Vapp.mat");
Vxy.save("../DATA/Vex.mat");
cout << nGrid << endl;
// exit(EXIT_SUCCESS);
#endif
cout << "test" << endl;
Vm = zeros<cx_vec>(M);
Vqr = zeros<cx_vec>(nGrid);
}
