public void SquareMatrixDifficultEigensystem()
{
// this requires > 30 iterations to converge, looses more accuracy than it should
// this example throws a NonConvergenceException without the ad hoc shift but works once the ad hoc shift is included
SquareMatrix M = new SquareMatrix(4);
M[0, 1] = 2;
M[0, 3] = -1;
M[1, 0] = 1;
M[2, 1] = 1;
M[3, 2] = 1;
// The eigenvalues of this matrix are -1, -1, 1, 1.
// There are only two distinct eigenvectors: (1, -1, 1, -1) with eigenvalue -1, and (1, 1, 1, 1) with eigenvalue 1.
ComplexEigensystem E = M.Eigensystem();
for (int i = 0; i < E.Dimension; i++)
{
Console.WriteLine(E.Eigenvalue(i));
for (int j = 0; j < E.Dimension; j++)
{
Console.Write("{0} ", E.Eigenvector(i)[j]);
}
Console.WriteLine();
Assert.IsTrue(TestUtilities.IsNearlyEigenpair(M, E.Eigenvector(i), E.Eigenvalue(i)));
}
}
public void SquareUnitMatrixEigensystem()
{
int d = 3;
SquareMatrix I = TestUtilities.CreateSquareUnitMatrix(d);
ComplexEigensystem E = I.Eigensystem();
Assert.IsTrue(E.Dimension == d);
for (int i = 0; i < d; i++)
{
Complex val = E.Eigenvalue(i);
Assert.IsTrue(val == 1.0);
Complex[] vec = E.Eigenvector(i);
for (int j = 0; j < d; j++)
{
if (i == j)
{
Assert.IsTrue(vec[j] == 1.0);
}
else
{
Assert.IsTrue(vec[j] == 0.0);
}
}
}
}
public void SquareVandermondeMatrixEigenvalues()
{
for (int d = 1; d <= 8; d++)
{
SquareMatrix H = CreateVandermondeMatrix(d);
double tr = H.Trace();
ComplexEigensystem E = H.Eigensystem();
double sum = 0.0;
for (int i = 0; i < d; i++)
{
Console.WriteLine(E.Eigenvalue(i));
double e = E.Eigenvalue(i).Re;
sum += e;
Complex[] vc = E.Eigenvector(i);
ColumnVector v = new ColumnVector(d);
for (int j = 0; j < d; j++)
{
//Console.WriteLine(" {0}", vc[j]);
v[j] = vc[j].Re;
}
ColumnVector Hv = H * v;
ColumnVector ev = e * v;
Assert.IsTrue(TestUtilities.IsNearlyEigenpair(H, v, e));
}
Assert.IsTrue(TestUtilities.IsNearlyEqual(tr, sum));
}
}
public void Bug7208()
{
// this matrix has two eigenpairs with the same eigenvalue but distinct eigenvectors
// we would report the same eigenvector for both, making the matrix of eigenvectors
// non-invertible
int n = 4;
SquareMatrix matrix = new SquareMatrix(n + 1);
double d = (3 + 2 * Math.Cos(2 * Math.PI / n));
double w = (64 * n) / (40 - d * d) - n;
double w1 = w / (w + n);
double w2 = 1 / (w + n);
matrix[0, 0] = w1;
for (int i = 1; i < n; i++)
{
matrix[0, i + 1] = w2;
matrix[i + 1, 0] = 3.0 / 8;
matrix[i + 1, i + 1] = 3.0 / 8;
matrix[i, i + 1] = 1.0 / 8;
matrix[i + 1, i] = 1.0 / 8;
}
matrix[0, 1] = w2;
matrix[1, 0] = 3.0 / 8;
matrix[1, 1] = 3.0 / 8;
matrix[1, n] = 1.0 / 8;
matrix[n, 1] = 1.0 / 8;
ComplexEigensystem ces = matrix.Eigensystem();
Console.WriteLine("output");
SquareMatrix V = new SquareMatrix(n + 1);
for (int i = 0; i < n + 1; i++)
{
Console.WriteLine("#{0}: {1}", i, ces.Eigenvalue(i));
for (int j = 0; j < n + 1; j++)
{
V[i, j] = ces.Eigenvector(i)[j].Re;
Console.WriteLine(V[i, j]);
}
}
SquareMatrix inv = V.Inverse();
}
public void Bug8021()
{
// User presented a matrix with a large number (138) of zero eigenvalues.
// QR algorithm got tripped up on high degeneracy, but eigenvalues could
// be revealed before QR by simple index permutation. Added code to isolate
// these "cheap" eigenvalues before starting QR algorithm.
int n = 276;
SquareMatrix A = new SquareMatrix(n);
using (System.IO.StreamReader reader = System.IO.File.OpenText(@"Bug8021.csv")) {
int r = 0;
while (!reader.EndOfStream)
{
string line = reader.ReadLine();
string[] cells = line.Split(',');
for (int c = 0; c < cells.Length; c++)
{
string cell = cells[c];
double value = Double.Parse(cell);
A[r, c] = value;
}
r++;
}
}
ComplexEigensystem S = A.Eigensystem();
for (int i = 0; i < S.Dimension; i++)
{
TestUtilities.IsNearlyEigenpair(A, S.Eigenvector(i), S.Eigenvalue(i));
}
Complex[] eigenvalues = A.Eigenvalues();
double trace = A.Trace();
Complex sum = 0.0;
for (int i = 0; i < eigenvalues.Length; i++)
{
sum += eigenvalues[i];
}
TestUtilities.IsNearlyEqual(trace, sum);
}
public void DegenerateEigenvalues()
{
double[,] souce = new double[, ] {
{ 1, 0, 1 },
{ 0, 2, 0 },
{ 1, 0, 1 }
};
SquareMatrix A = new SquareMatrix(3);
for (int r = 0; r < souce.GetLength(0); r++)
{
for (int c = 0; c < souce.GetLength(1); c++)
{
A[r, c] = souce[r, c];
}
}
ComplexEigensystem e = A.Eigensystem();
}
public void SquareRandomMatrixEigenvalues()
{
for (int d = 1; d <= 67; d = d + 11)
{
SquareMatrix I = TestUtilities.CreateSquareUnitMatrix(d);
SquareMatrix M = CreateSquareRandomMatrix(d, d);
double tr = M.Trace();
DateTime start = DateTime.Now;
ComplexEigensystem E = M.Eigensystem();
DateTime finish = DateTime.Now;
Console.WriteLine("d={0} t={1} ms", d, (finish - start).Milliseconds);
Assert.IsTrue(E.Dimension == d);
Complex[] es = new Complex[d];
for (int i = 0; i < d; i++)
{
es[i] = E.Eigenvalue(i);
Complex[] v = E.Eigenvector(i);
Assert.IsTrue(TestUtilities.IsNearlyEigenpair(M, v, es[i]));
}
Assert.IsTrue(TestUtilities.IsSumNearlyEqual(es, tr));
}
}
public void CompanionMatrixEigenvalues()
{
SquareMatrix CM = new SquareMatrix(3);
CM[0, 2] = -1.0;
CM[1, 0] = 1.0;
CM[1, 2] = -3.0;
CM[2, 1] = 1.0;
CM[2, 2] = -3.0;
ComplexEigensystem EM = CM.Eigensystem();
for (int i = 0; i < EM.Dimension; i++)
{
Console.WriteLine("! {0}", EM.Eigenvalue(i));
}
for (int d = 2; d <= 8; d++)
{
Console.WriteLine(d);
SquareMatrix C = new SquareMatrix(d);
for (int r = 1; r < d; r++)
{
C[r, r - 1] = 1.0;
}
for (int r = 0; r < d; r++)
{
C[r, d - 1] = -AdvancedIntegerMath.BinomialCoefficient(d, r);
}
ComplexEigensystem e = C.Eigensystem();
for (int i = 0; i < e.Dimension; i++)
{
Console.WriteLine("!! {0}", e.Eigenvalue(i));
}
}
}
public void SquareMatrixStochasticEigensystem()
{
// this is a simplifed form of a Markov matrix that arose in the Monopoly problem
// and failed to converge
int n = 12;
// originally failed for n=12 due to lack of 2x2 detection at top
// now tested for n up to 40, but n=14 appears to still be a problem,
// probably due to a six-times degenerate eigenvalue
SquareMatrix R = new SquareMatrix(n);
for (int c = 0; c < n; c++)
{
R[(c + 2) % n, c] = 1.0 / 36.0;
R[(c + 3) % n, c] = 2.0 / 36.0;
R[(c + 4) % n, c] = 3.0 / 36.0;
R[(c + 5) % n, c] = 4.0 / 36.0;
R[(c + 6) % n, c] = 5.0 / 36.0;
R[(c + 7) % n, c] = 6.0 / 36.0;
R[(c + 8) % n, c] = 5.0 / 36.0;
R[(c + 9) % n, c] = 4.0 / 36.0;
R[(c + 10) % n, c] = 3.0 / 36.0;
R[(c + 11) % n, c] = 2.0 / 36.0;
R[(c + 12) % n, c] = 1.0 / 36.0;
}
//Complex[] v = R.Eigenvalues();
ComplexEigensystem E = R.Eigensystem();
for (int i = 0; i < E.Dimension; i++)
{
Console.WriteLine(E.Eigenvalue(i));
Assert.IsTrue(TestUtilities.IsNearlyEigenpair(R, E.Eigenvector(i), E.Eigenvalue(i)));
}
}
