void diagonal(int a,int b)
{
int i=a,j=b;
if(i>=length-1 && j>=length-1)diag1(i,j);//nagore i nalqwo
if(i>=length-1 && j<=size-length)diag2(i,j);//nagore i nadqsno
if(i<=size-length && j>=length-1)diag3(i,j);//nadolu i nalqwo
if(i<=size-length && j<=size-length)diag4(i,j);//nadolu i nadqsno
}
void invertMatrix(Int_t msize=6)
{
#ifdef __CINT__
gSystem->Load("libMatrix");
#endif
if (msize < 2 || msize > 10) {
cout << "2 <= msize <= 10" <<endl;
return;
}
cout << "--------------------------------------------------------" <<endl;
cout << "Inversion results for a ("<<msize<<","<<msize<<") matrix" <<endl;
cout << "For each inversion procedure we check the maximum size " <<endl;
cout << "of the off-diagonal elements of Inv(A) * A " <<endl;
cout << "--------------------------------------------------------" <<endl;
TMatrixD H_square = THilbertMatrixD(msize,msize);
// 1. InvertFast(Double_t *det=0)
// It is identical to Invert() for sizes > 6 x 6 but for smaller sizes, the
// inversion is performed according to Cramer's rule by explicitly calculating
// all Jacobi's sub-determinants . For instance for a 6 x 6 matrix this means:
// # of 5 x 5 determinant : 36
// # of 4 x 4 determinant : 75
// # of 3 x 3 determinant : 80
// # of 2 x 2 determinant : 45 (see TMatrixD/FCramerInv.cxx)
//
// The only "quality" control in this process is to check whether the 6 x 6
// determinant is unequal 0 . But speed gains are significant compared to Invert() ,
// up to an order of magnitude for sizes <= 4 x 4
//
// The inversion is done "in place", so the original matrix will be overwritten
// If a pointer to a Double_t is supplied the determinant is calculated
//
cout << "1. Use .InvertFast(&det)" <<endl;
if (msize > 6)
cout << " for ("<<msize<<","<<msize<<") this is identical to .Invert(&det)" <<endl;
Double_t det1;
TMatrixD H1 = H_square;
H1.InvertFast(&det1);
// Get the maximum off-diagonal matrix value . One way to do this is to set the
// diagonal to zero .
TMatrixD U1(H1,TMatrixD::kMult,H_square);
TMatrixDDiag diag1(U1); diag1 = 0.0;
const Double_t U1_max_offdiag = (U1.Abs()).Max();
cout << " Maximum off-diagonal = " << U1_max_offdiag << endl;
cout << " Determinant = " << det1 <<endl;
// 2. Invert(Double_t *det=0)
// Again the inversion is performed in place .
// It consists out of a sequence of calls to the decomposition classes . For instance
// for the general dense matrix TMatrixD the LU decomposition is invoked:
// - The matrix is decomposed using a scheme according to Crout which involves
// "implicit partial pivoting", see for instance Num. Recip. (we have also available
// a decomposition scheme that does not the scaling and is therefore even slightly
// faster but less stable)
// With each decomposition, a tolerance has to be specified . If this tolerance
// requirement is not met, the matrix is regarded as being singular. The value
// passed to this decomposition, is the data member fTol of the matrix . Its
// default value is DBL_EPSILON, which is defined as the smallest number so that
// 1+DBL_EPSILON > 1
// - The last step is a standard forward/backward substitution .
//
// It is important to realize that both InvertFast() and Invert() are "one-shot" deals , speed
// comes at a price . If something goes wrong because the matrix is (near) singular, you have
// overwritten your original matrix and no factorization is available anymore to get more
// information like condition number or change the tolerance number .
//
// All other calls in the matrix classes involving inversion like the ones with the "smart"
// constructors (kInverted,kInvMult...) use this inversion method .
//
cout << "2. Use .Invert(&det)" <<endl;
Double_t det2;
TMatrixD H2 = H_square;
H2.Invert(&det2);
TMatrixD U2(H2,TMatrixD::kMult,H_square);
TMatrixDDiag diag2(U2); diag2 = 0.0;
const Double_t U2_max_offdiag = (U2.Abs()).Max();
cout << " Maximum off-diagonal = " << U2_max_offdiag << endl;
cout << " Determinant = " << det2 <<endl;
// 3. Inversion through LU decomposition
// The (default) algorithms used are similar to 2. (Not identical because in 2, the whole
// calculation is done "in-place". Here the original matrix is copied (so more memory
// management => slower) and several operations can be performed without having to repeat
// the decomposition step .
// Inverting a matrix is nothing else than solving a set of equations where the rhs is given
// by the unit matrix, so the steps to take are identical to those solving a linear equation :
//
cout << "3. Use TDecompLU" <<endl;
TMatrixD H3 = H_square;
TDecompLU lu(H_square);
// Any operation that requires a decomposition will trigger it . The class keeps
// an internal state so that following operations will not perform the decomposition again
// unless the matrix is changed through SetMatrix(..)
// One might want to proceed more cautiously by invoking first Decompose() and check its
// return value before proceeding....
lu.Invert(H3);
Double_t d1_lu; Double_t d2_lu;
lu.Det(d1_lu,d2_lu);
Double_t det3 = d1_lu*TMath::Power(2.,d2_lu);
TMatrixD U3(H3,TMatrixD::kMult,H_square);
TMatrixDDiag diag3(U3); diag3 = 0.0;
const Double_t U3_max_offdiag = (U3.Abs()).Max();
cout << " Maximum off-diagonal = " << U3_max_offdiag << endl;
cout << " Determinant = " << det3 <<endl;
// 4. Inversion through SVD decomposition
// For SVD and QRH, the (n x m) matrix does only have to fulfill n >=m . In case n > m
// a pseudo-inverse is calculated
cout << "4. Use TDecompSVD on non-square matrix" <<endl;
TMatrixD H_nsquare = THilbertMatrixD(msize,msize-1);
TDecompSVD svd(H_nsquare);
TMatrixD H4 = svd.Invert();
Double_t d1_svd; Double_t d2_svd;
svd.Det(d1_svd,d2_svd);
Double_t det4 = d1_svd*TMath::Power(2.,d2_svd);
TMatrixD U4(H4,TMatrixD::kMult,H_nsquare);
TMatrixDDiag diag4(U4); diag4 = 0.0;
const Double_t U4_max_offdiag = (U4.Abs()).Max();
cout << " Maximum off-diagonal = " << U4_max_offdiag << endl;
cout << " Determinant = " << det4 <<endl;
}