/// <summary>
/// Computes the eigenvalues and eigenvectors of the matrix.
/// </summary>
/// <returns>A decomposition that makes the eigenvalues and eigenvectors manifest.</returns>
/// <remarks>
/// <para>For a generic vector v and matrix M, Mv = u will point in some direction with no particular relationship to v.
/// The eigenvectors of a matrix M are vectors z that satisfy Mz = λz, i.e. multiplying an eigenvector by a
/// matrix reproduces the same vector, up to a proportionality constant λ called the eigenvalue.</para>
/// <para>For v to be an eigenvector of M with eigenvalue λ, (M - λI)z = 0. But for a matrix to
/// annihilate any non-zero vector, that matrix's determinant must vanish, so det(M - λI)=0. For a matrix of
/// order N, this is an equation for the roots of a polynomial of order N. Since an order-N polynomial always has exactly
/// N roots, an order-N matrix always has exactly N eigenvalues.</para>
/// <para>Since a polynomial with real coefficients can still have complex roots, a real square matrix can nonetheless
/// have complex eigenvalues (and corresponding complex eigenvectors). However, again like the complex roots of a real
/// polynomial, such eigenvalues will always occurs in complex-conjugate pairs.</para>
/// <para>Although the eigenvalue polynomial ensures that an order-N matrix has N eigenvalues, it can occur that there
/// are not N corresponding independent eigenvectors. A matrix with fewer eigenvectors than eigenvalues is called
/// defective. Like singularity, defectiveness represents a delicate balance between the elements of a matrix that can
/// typically be disturbed by just an infinitesimal perturbation of elements. Because of round-off-error, then, floating-point
/// algorithms cannot reliably identify defective matrices. Instead, this method will return a full set of eigenvectors,
/// but some eigenvectors, corresponding to very nearly equal eigenvalues, will be very nearly parallel.</para>
/// <para>While a generic square matrix can be defective, many subspecies of square matrices are guaranteed not to be.
/// This includes Markov matrices, orthogonal matrices, and symmetric matrices.</para>
/// <para>Determining the eigenvalues and eigenvectors of a matrix is an O(N<sup>3</sup>) operation. If you need only the
/// eigenvalues of a matrix, the <see cref="Eigenvalues"/> method is more efficient.</para>
/// </remarks>
/// <seealso href="https://en.wikipedia.org/wiki/Eigendecomposition_of_a_matrix"/>
public ComplexEigendecomposition Eigendecomposition()
{
//double[] aStore = MatrixAlgorithms.Copy(store, dimension, dimension);
double[] aStore = MatrixAlgorithms.Copy(store, offset, rowStride, colStride, dimension, dimension);
int[] perm = new int[dimension];
for (int i = 0; i < perm.Length; i++)
{
perm[i] = i;
}
SquareMatrixAlgorithms.IsolateCheapEigenvalues(aStore, perm, dimension);
double[] qStore = MatrixAlgorithms.AllocateStorage(dimension, dimension);
for (int i = 0; i < perm.Length; i++)
{
MatrixAlgorithms.SetEntry(qStore, dimension, dimension, perm[i], i, 1.0);
}
//double[] qStore = SquareMatrixAlgorithms.CreateUnitMatrix(dimension);
// Reduce the original matrix to Hessenberg form
SquareMatrixAlgorithms.ReduceToHessenberg(aStore, qStore, dimension);
// Reduce the Hessenberg matrix to real Schur form
SquareMatrixAlgorithms.ReduceToRealSchurForm(aStore, qStore, dimension);
//SquareMatrix A = new SquareMatrix(aStore, dimension);
SquareMatrix Q = new SquareMatrix(qStore, dimension);
Complex[] eigenvalues; Complex[][] eigenvectors;
// Extract the eigenvalues and eigenvectors of the Schur form matrix
SquareMatrixAlgorithms.SchurEigensystem(aStore, dimension, out eigenvalues, out eigenvectors);
// transform eigenvectors of schur form into eigenvectors of original matrix
// while we are at it, normalize so largest component has value 1
for (int i = 0; i < dimension; i++)
{
Complex[] v = new Complex[dimension];
double norm = 0.0;
for (int j = 0; j < dimension; j++)
{
for (int k = 0; k < dimension; k++)
{
v[j] += Q[j, k] * eigenvectors[i][k];
}
norm = Math.Max(norm, Math.Max(Math.Abs(v[j].Re), Math.Abs(v[j].Im)));
}
for (int j = 0; j < dimension; j++)
{
v[j] = v[j] / norm;
}
eigenvectors[i] = v;
}
ComplexEigendecomposition eigensystem = new ComplexEigendecomposition(dimension, eigenvalues, eigenvectors);
return(eigensystem);
}
internal ComplexEigenpairCollection(ComplexEigendecomposition system)
{
Debug.Assert(system != null);
this.system = system;
}
