int new_linearisation(struct jacobian *jac,double hinvGak,int neq,ErrorMsg error_message){
double luparity, *Ax;
int i,j,*Ap,*Ai,funcreturn;
if(jac->use_sparse==1){
Ap = jac->spJ->Ap; Ai = jac->spJ->Ai; Ax = jac->spJ->Ax;
/* Construct jac->spJ->Ax from jac->xjac, the jacobian:*/
for(j=0;j<neq;j++){
for(i=Ap[j];i<Ap[j+1];i++){
if(Ai[i]==j){
/* I'm at the diagonal */
Ax[i] = 1.0-hinvGak*jac->xjac[i];
}
else{
Ax[i] = -hinvGak*jac->xjac[i];
}
}
}
/* Matrix constructed... */
if(jac->new_jacobian==_TRUE_){
/*I have a new pattern, and I have not done a LU decomposition
since the last jacobian calculation, so I need to do a full
sparse LU-decomposition: */
/* Find the sparsity pattern C = J + J':*/
calc_C(jac);
/* Calculate the optimal ordering: */
sp_amd(jac->Cp, jac->Ci, neq, jac->cnzmax,
jac->Numerical->q,jac->Numerical->wamd);
/* if the next line is uncomented, the code uses natural ordering instead of AMD ordering */
/*jac->Numerical->q = NULL;*/
funcreturn = sp_ludcmp(jac->Numerical, jac->spJ, 1e-3);
class_test(funcreturn == _FAILURE_,error_message,
"Failure in sp_ludcmp. Possibly singular matrix!");
jac->new_jacobian = _FALSE_;
}
else{
/* I have a repeated pattern, so I can just refactor:*/
sp_refactor(jac->Numerical, jac->spJ);
}
}
else{
/* Normal calculation: */
for(i=1;i<=neq;i++){
for(j=1;j<=neq;j++){
jac->LU[i][j] = - hinvGak * jac->dfdy[i][j];
if(i==j) jac->LU[i][j] +=1.0;
}
}
/*Dense LU decomposition: */
funcreturn = ludcmp(jac->LU,neq,jac->luidx,&luparity,jac->LUw);
class_test(funcreturn == _FAILURE_,error_message,
"Failure in ludcmp. Possibly singular matrix!");
}
return _SUCCESS_;
}
MatrixXcf Circuit_Handler::calc_S(){
MatrixXcf S(num_of_nodes,num_of_nodes);
MatrixXcf I = MatrixXcf::Identity(num_of_nodes,num_of_nodes);
MatrixXcf X = calc_X();
MatrixXcf C = calc_C();
num_of_ports = ports.size();
MatrixXcf temp2(num_of_nodes,num_of_nodes);
MatrixXcf temp(num_of_nodes,num_of_nodes);
temp = C*X;
temp = I - temp;
temp2 = temp.inverse();
S = X*temp2;
return S;
}
