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Grid/lib/algorithms/iterative/Francis.h

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#ifndef FRANCIS_H
#define FRANCIS_H
#include <cstdlib>
#include <string>
#include <cmath>
#include <iostream>
#include <sstream>
#include <stdexcept>
#include <fstream>
#include <complex>
#include <algorithm>
//#include <timer.h>
//#include <lapacke.h>
//#include <Eigen/Dense>
namespace Grid {
template <class T> int SymmEigensystem(DenseMatrix<T > &Ain, DenseVector<T> &evals, DenseMatrix<T> &evecs, RealD small);
template <class T> int Eigensystem(DenseMatrix<T > &Ain, DenseVector<T> &evals, DenseMatrix<T> &evecs, RealD small);
/**
Find the eigenvalues of an upper hessenberg matrix using the Francis QR algorithm.
H =
x x x x x x x x x
x x x x x x x x x
0 x x x x x x x x
0 0 x x x x x x x
0 0 0 x x x x x x
0 0 0 0 x x x x x
0 0 0 0 0 x x x x
0 0 0 0 0 0 x x x
0 0 0 0 0 0 0 x x
Factorization is P T P^H where T is upper triangular (mod cc blocks) and P is orthagonal/unitary.
**/
template <class T>
int QReigensystem(DenseMatrix<T> &Hin, DenseVector<T> &evals, DenseMatrix<T> &evecs, RealD small)
{
DenseMatrix<T> H = Hin;
int N ; SizeSquare(H,N);
int M = N;
Fill(evals,0);
Fill(evecs,0);
T s,t,x=0,y=0,z=0;
T u,d;
T apd,amd,bc;
DenseVector<T> p(N,0);
T nrm = Norm(H); ///DenseMatrix Norm
int n, m;
int e = 0;
int it = 0;
int tot_it = 0;
int l = 0;
int r = 0;
DenseMatrix<T> P; Resize(P,N,N); Unity(P);
DenseVector<int> trows(N,0);
/// Check if the matrix is really hessenberg, if not abort
RealD sth = 0;
for(int j=0;j<N;j++){
for(int i=j+2;i<N;i++){
sth = abs(H[i][j]);
if(sth > small){
std::cout << "Non hessenberg H = " << sth << " > " << small << std::endl;
exit(1);
}
}
}
do{
std::cout << "Francis QR Step N = " << N << std::endl;
/** Check for convergence
x x x x x
0 x x x x
0 0 x x x
0 0 x x x
0 0 0 0 x
for this matrix l = 4
**/
do{
l = Chop_subdiag(H,nrm,e,small);
r = 0; ///May have converged on more than one eval
///Single eval
if(l == N-1){
evals[e] = H[l][l];
N--; e++; r++; it = 0;
}
///RealD eval
if(l == N-2){
trows[l+1] = 1; ///Needed for UTSolve
apd = H[l][l] + H[l+1][l+1];
amd = H[l][l] - H[l+1][l+1];
bc = (T)4.0*H[l+1][l]*H[l][l+1];
evals[e] = (T)0.5*( apd + sqrt(amd*amd + bc) );
evals[e+1] = (T)0.5*( apd - sqrt(amd*amd + bc) );
N-=2; e+=2; r++; it = 0;
}
} while(r>0);
if(N ==0) break;
DenseVector<T > ck; Resize(ck,3);
DenseVector<T> v; Resize(v,3);
for(int m = N-3; m >= l; m--){
///Starting vector essentially random shift.
if(it%10 == 0 && N >= 3 && it > 0){
s = (T)1.618033989*( abs( H[N-1][N-2] ) + abs( H[N-2][N-3] ) );
t = (T)0.618033989*( abs( H[N-1][N-2] ) + abs( H[N-2][N-3] ) );
x = H[m][m]*H[m][m] + H[m][m+1]*H[m+1][m] - s*H[m][m] + t;
y = H[m+1][m]*(H[m][m] + H[m+1][m+1] - s);
z = H[m+1][m]*H[m+2][m+1];
}
///Starting vector implicit Q theorem
else{
s = (H[N-2][N-2] + H[N-1][N-1]);
t = (H[N-2][N-2]*H[N-1][N-1] - H[N-2][N-1]*H[N-1][N-2]);
x = H[m][m]*H[m][m] + H[m][m+1]*H[m+1][m] - s*H[m][m] + t;
y = H[m+1][m]*(H[m][m] + H[m+1][m+1] - s);
z = H[m+1][m]*H[m+2][m+1];
}
ck[0] = x; ck[1] = y; ck[2] = z;
if(m == l) break;
/** Some stupid thing from numerical recipies, seems to work**/
// PAB.. for heaven's sake quote page, purpose, evidence it works.
// what sort of comment is that!?!?!?
u=abs(H[m][m-1])*(abs(y)+abs(z));
d=abs(x)*(abs(H[m-1][m-1])+abs(H[m][m])+abs(H[m+1][m+1]));
if ((T)abs(u+d) == (T)abs(d) ){
l = m; break;
}
//if (u < small){l = m; break;}
}
if(it > 100000){
std::cout << "QReigensystem: bugger it got stuck after 100000 iterations" << std::endl;
std::cout << "got " << e << " evals " << l << " " << N << std::endl;
exit(1);
}
normalize(ck); ///Normalization cancels in PHP anyway
T beta;
Householder_vector<T >(ck, 0, 2, v, beta);
Householder_mult<T >(H,v,beta,0,l,l+2,0);
Householder_mult<T >(H,v,beta,0,l,l+2,1);
///Accumulate eigenvector
Householder_mult<T >(P,v,beta,0,l,l+2,1);
int sw = 0; ///Are we on the last row?
for(int k=l;k<N-2;k++){
x = H[k+1][k];
y = H[k+2][k];
z = (T)0.0;
if(k+3 <= N-1){
z = H[k+3][k];
} else{
sw = 1;
v[2] = (T)0.0;
}
ck[0] = x; ck[1] = y; ck[2] = z;
normalize(ck);
Householder_vector<T >(ck, 0, 2-sw, v, beta);
Householder_mult<T >(H,v, beta,0,k+1,k+3-sw,0);
Householder_mult<T >(H,v, beta,0,k+1,k+3-sw,1);
///Accumulate eigenvector
Householder_mult<T >(P,v, beta,0,k+1,k+3-sw,1);
}
it++;
tot_it++;
}while(N > 1);
N = evals.size();
///Annoying - UT solves in reverse order;
DenseVector<T> tmp; Resize(tmp,N);
for(int i=0;i<N;i++){
tmp[i] = evals[N-i-1];
}
evals = tmp;
UTeigenvectors(H, trows, evals, evecs);
for(int i=0;i<evals.size();i++){evecs[i] = P*evecs[i]; normalize(evecs[i]);}
return tot_it;
}
template <class T>
int my_Wilkinson(DenseMatrix<T> &Hin, DenseVector<T> &evals, DenseMatrix<T> &evecs, RealD small)
{
/**
Find the eigenvalues of an upper Hessenberg matrix using the Wilkinson QR algorithm.
H =
x x 0 0 0 0
x x x 0 0 0
0 x x x 0 0
0 0 x x x 0
0 0 0 x x x
0 0 0 0 x x
Factorization is P T P^H where T is upper triangular (mod cc blocks) and P is orthagonal/unitary. **/
return my_Wilkinson(Hin, evals, evecs, small, small);
}
template <class T>
int my_Wilkinson(DenseMatrix<T> &Hin, DenseVector<T> &evals, DenseMatrix<T> &evecs, RealD small, RealD tol)
{
int N; SizeSquare(Hin,N);
int M = N;
///I don't want to modify the input but matricies must be passed by reference
//Scale a matrix by its "norm"
//RealD Hnorm = abs( Hin.LargestDiag() ); H = H*(1.0/Hnorm);
DenseMatrix<T> H; H = Hin;
RealD Hnorm = abs(Norm(Hin));
H = H * (1.0 / Hnorm);
// TODO use openmp and memset
Fill(evals,0);
Fill(evecs,0);
T s, t, x = 0, y = 0, z = 0;
T u, d;
T apd, amd, bc;
DenseVector<T> p; Resize(p,N); Fill(p,0);
T nrm = Norm(H); ///DenseMatrix Norm
int n, m;
int e = 0;
int it = 0;
int tot_it = 0;
int l = 0;
int r = 0;
DenseMatrix<T> P; Resize(P,N,N);
Unity(P);
DenseVector<int> trows(N, 0);
/// Check if the matrix is really symm tridiag
RealD sth = 0;
for(int j = 0; j < N; ++j)
{
for(int i = j + 2; i < N; ++i)
{
if(abs(H[i][j]) > tol || abs(H[j][i]) > tol)
{
std::cout << "Non Tridiagonal H(" << i << ","<< j << ") = |" << Real( real( H[j][i] ) ) << "| > " << tol << std::endl;
std::cout << "Warning tridiagonalize and call again" << std::endl;
// exit(1); // see what is going on
//return;
}
}
}
do{
do{
//Jasper
//Check if the subdiagonal term is small enough (<small)
//if true then it is converged.
//check start from H.dim - e - 1
//How to deal with more than 2 are converged?
//What if Chop_symm_subdiag return something int the middle?
//--------------
l = Chop_symm_subdiag(H,nrm, e, small);
r = 0; ///May have converged on more than one eval
//Jasper
//In this case
// x x 0 0 0 0
// x x x 0 0 0
// 0 x x x 0 0
// 0 0 x x x 0
// 0 0 0 x x 0
// 0 0 0 0 0 x <- l
//--------------
///Single eval
if(l == N - 1)
{
evals[e] = H[l][l];
N--;
e++;
r++;
it = 0;
}
//Jasper
// x x 0 0 0 0
// x x x 0 0 0
// 0 x x x 0 0
// 0 0 x x 0 0
// 0 0 0 0 x x <- l
// 0 0 0 0 x x
//--------------
///RealD eval
if(l == N - 2)
{
trows[l + 1] = 1; ///Needed for UTSolve
apd = H[l][l] + H[l + 1][ l + 1];
amd = H[l][l] - H[l + 1][l + 1];
bc = (T) 4.0 * H[l + 1][l] * H[l][l + 1];
evals[e] = (T) 0.5 * (apd + sqrt(amd * amd + bc));
evals[e + 1] = (T) 0.5 * (apd - sqrt(amd * amd + bc));
N -= 2;
e += 2;
r++;
it = 0;
}
}while(r > 0);
//Jasper
//Already converged
//--------------
if(N == 0) break;
DenseVector<T> ck,v; Resize(ck,2); Resize(v,2);
for(int m = N - 3; m >= l; m--)
{
///Starting vector essentially random shift.
if(it%10 == 0 && N >= 3 && it > 0)
{
t = abs(H[N - 1][N - 2]) + abs(H[N - 2][N - 3]);
x = H[m][m] - t;
z = H[m + 1][m];
} else {
///Starting vector implicit Q theorem
d = (H[N - 2][N - 2] - H[N - 1][N - 1]) * (T) 0.5;
t = H[N - 1][N - 1] - H[N - 1][N - 2] * H[N - 1][N - 2]
/ (d + sign(d) * sqrt(d * d + H[N - 1][N - 2] * H[N - 1][N - 2]));
x = H[m][m] - t;
z = H[m + 1][m];
}
//Jasper
//why it is here????
//-----------------------
if(m == l)
break;
u = abs(H[m][m - 1]) * (abs(y) + abs(z));
d = abs(x) * (abs(H[m - 1][m - 1]) + abs(H[m][m]) + abs(H[m + 1][m + 1]));
if ((T)abs(u + d) == (T)abs(d))
{
l = m;
break;
}
}
//Jasper
if(it > 1000000)
{
std::cout << "Wilkinson: bugger it got stuck after 100000 iterations" << std::endl;
std::cout << "got " << e << " evals " << l << " " << N << std::endl;
exit(1);
}
//
T s, c;
Givens_calc<T>(x, z, c, s);
Givens_mult<T>(H, l, l + 1, c, -s, 0);
Givens_mult<T>(H, l, l + 1, c, s, 1);
Givens_mult<T>(P, l, l + 1, c, s, 1);
//
for(int k = l; k < N - 2; ++k)
{
x = H.A[k + 1][k];
z = H.A[k + 2][k];
Givens_calc<T>(x, z, c, s);
Givens_mult<T>(H, k + 1, k + 2, c, -s, 0);
Givens_mult<T>(H, k + 1, k + 2, c, s, 1);
Givens_mult<T>(P, k + 1, k + 2, c, s, 1);
}
it++;
tot_it++;
}while(N > 1);
N = evals.size();
///Annoying - UT solves in reverse order;
DenseVector<T> tmp(N);
for(int i = 0; i < N; ++i)
tmp[i] = evals[N-i-1];
evals = tmp;
//
UTeigenvectors(H, trows, evals, evecs);
//UTSymmEigenvectors(H, trows, evals, evecs);
for(int i = 0; i < evals.size(); ++i)
{
evecs[i] = P * evecs[i];
normalize(evecs[i]);
evals[i] = evals[i] * Hnorm;
}
// // FIXME this is to test
// Hin.write("evecs3", evecs);
// Hin.write("evals3", evals);
// // check rsd
// for(int i = 0; i < M; i++) {
// vector<T> Aevec = Hin * evecs[i];
// RealD norm2(0.);
// for(int j = 0; j < M; j++) {
// norm2 += (Aevec[j] - evals[i] * evecs[i][j]) * (Aevec[j] - evals[i] * evecs[i][j]);
// }
// }
return tot_it;
}
template <class T>
void Hess(DenseMatrix<T > &A, DenseMatrix<T> &Q, int start){
/**
turn a matrix A =
x x x x x
x x x x x
x x x x x
x x x x x
x x x x x
into
x x x x x
x x x x x
0 x x x x
0 0 x x x
0 0 0 x x
with householder rotations
Slow.
*/
int N ; SizeSquare(A,N);
DenseVector<T > p; Resize(p,N); Fill(p,0);
for(int k=start;k<N-2;k++){
//cerr << "hess" << k << std::endl;
DenseVector<T > ck,v; Resize(ck,N-k-1); Resize(v,N-k-1);
for(int i=k+1;i<N;i++){ck[i-k-1] = A(i,k);} ///kth column
normalize(ck); ///Normalization cancels in PHP anyway
T beta;
Householder_vector<T >(ck, 0, ck.size()-1, v, beta); ///Householder vector
Householder_mult<T>(A,v,beta,start,k+1,N-1,0); ///A -> PA
Householder_mult<T >(A,v,beta,start,k+1,N-1,1); ///PA -> PAP^H
///Accumulate eigenvector
Householder_mult<T >(Q,v,beta,start,k+1,N-1,1); ///Q -> QP^H
}
/*for(int l=0;l<N-2;l++){
for(int k=l+2;k<N;k++){
A(0,k,l);
}
}*/
}
template <class T>
void Tri(DenseMatrix<T > &A, DenseMatrix<T> &Q, int start){
///Tridiagonalize a matrix
int N; SizeSquare(A,N);
Hess(A,Q,start);
/*for(int l=0;l<N-2;l++){
for(int k=l+2;k<N;k++){
A(0,l,k);
}
}*/
}
template <class T>
void ForceTridiagonal(DenseMatrix<T> &A){
///Tridiagonalize a matrix
int N ; SizeSquare(A,N);
for(int l=0;l<N-2;l++){
for(int k=l+2;k<N;k++){
A[l][k]=0;
A[k][l]=0;
}
}
}
template <class T>
int my_SymmEigensystem(DenseMatrix<T > &Ain, DenseVector<T> &evals, DenseVector<DenseVector<T> > &evecs, RealD small){
///Solve a symmetric eigensystem, not necessarily in tridiagonal form
int N; SizeSquare(Ain,N);
DenseMatrix<T > A; A = Ain;
DenseMatrix<T > Q; Resize(Q,N,N); Unity(Q);
Tri(A,Q,0);
int it = my_Wilkinson<T>(A, evals, evecs, small);
for(int k=0;k<N;k++){evecs[k] = Q*evecs[k];}
return it;
}
template <class T>
int Wilkinson(DenseMatrix<T> &Ain, DenseVector<T> &evals, DenseVector<DenseVector<T> > &evecs, RealD small){
return my_Wilkinson(Ain, evals, evecs, small);
}
template <class T>
int SymmEigensystem(DenseMatrix<T> &Ain, DenseVector<T> &evals, DenseVector<DenseVector<T> > &evecs, RealD small){
return my_SymmEigensystem(Ain, evals, evecs, small);
}
template <class T>
int Eigensystem(DenseMatrix<T > &Ain, DenseVector<T> &evals, DenseVector<DenseVector<T> > &evecs, RealD small){
///Solve a general eigensystem, not necessarily in tridiagonal form
int N = Ain.dim;
DenseMatrix<T > A(N); A = Ain;
DenseMatrix<T > Q(N);Q.Unity();
Hess(A,Q,0);
int it = QReigensystem<T>(A, evals, evecs, small);
for(int k=0;k<N;k++){evecs[k] = Q*evecs[k];}
return it;
}
}
#endif
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