private static bool EnsureSuccess(SpectraResult result)
{
try
{
result.EnsureSuccess();
return(true);
}
catch (SpectraException e)
{
Console.WriteLine(e.Message);
}
return(false);
}
/// <summary>
/// Prints eigenvalues and eigenvectors of nonsymmetric generalized eigen-problems.
/// </summary>
public static void General(SparseMatrix A, SparseMatrix B, SpectraResult result, bool shift, bool cshift = false)
{
if (!EnsureSuccess(result))
{
return;
}
int n = A.RowCount;
int nconv = result.ConvergedEigenValues;
Console.WriteLine();
Console.WriteLine("Testing ARPACK++ class ARluNonSymGenEig");
Console.WriteLine("Real nonsymmetric generalized eigenvalue problem: A*x - lambda*B*x");
Console.WriteLine(!shift ? "Regular mode" :
(cshift ? "Shift and invert mode (using the imaginary part of OP)" : "Shift and invert mode (using the real part of OP)"));
Console.WriteLine();
Console.WriteLine("Dimension of the system : " + n);
Console.WriteLine("Number of 'requested' eigenvalues : " + result.Count);
Console.WriteLine("Number of 'converged' eigenvalues : " + nconv);
Console.WriteLine("Number of Arnoldi vectors generated: " + result.ArnoldiCount);
Console.WriteLine("Number of iterations taken : " + result.IterationsTaken);
Console.WriteLine();
var evals = result.EigenValues;
var evecs = result.EigenVectors;
// Printing eigenvalues.
Console.WriteLine("Eigenvalues:");
for (int i = 0; i < nconv; i++)
{
Console.WriteLine(" lambda[" + (i + 1) + "]: " + evals[i]);
}
Console.WriteLine();
if (evecs != null)
{
// Printing the residual norm || A*x - lambda*B*x ||
// for the nconv accurately computed eigenvectors.
var x = new Complex[n];
var y = new Complex[n];
var r = new double[nconv]; // residuals
for (int i = 0; i < nconv; i++)
{
var lambda = evals[i];
evecs.Column(i, x);
CVector.Copy(x, y);
// y = B*x
B.Multiply(x, y);
// y = A*x - lambda*B*x
A.Multiply(1.0, x, -lambda, y);
r[i] = CVector.Norm(y) / Complex.Abs(lambda);
}
for (int i = 0; i < nconv; i++)
{
Console.WriteLine("||A*x(" + i + ") - lambda(" + i + ")*B*x(" + i + ")||: " + r[i]);
}
Console.WriteLine();
}
}
/// <summary>
/// Prints eigenvalues and eigenvectors of symmetric generalized eigen-problems.
/// </summary>
public static void Symmetric(SparseMatrix A, SparseMatrix B, SpectraResult result, ShiftMode mode)
{
if (!EnsureSuccess(result))
{
return;
}
int n = A.RowCount;
int nconv = result.ConvergedEigenValues;
Console.WriteLine();
Console.WriteLine("Testing ARPACK++ class ARluSymGenEig");
Console.WriteLine("Real symmetric generalized eigenvalue problem: A*x - lambda*B*x");
Console.WriteLine();
switch (mode)
{
case ShiftMode.None:
Console.WriteLine("Regular mode");
break;
case ShiftMode.Regular:
Console.WriteLine("Shift and invert mode");
break;
case ShiftMode.Buckling:
Console.WriteLine("Buckling mode");
break;
case ShiftMode.Cayley:
Console.WriteLine("Cayley mode");
break;
}
Console.WriteLine();
Console.WriteLine("Dimension of the system : " + n);
Console.WriteLine("Number of 'requested' eigenvalues : " + result.Count);
Console.WriteLine("Number of 'converged' eigenvalues : " + nconv);
Console.WriteLine("Number of Arnoldi vectors generated: " + result.ArnoldiCount);
Console.WriteLine("Number of iterations taken : " + result.IterationsTaken);
Console.WriteLine();
var evals = result.EigenValuesReal();
var evecs = result.EigenVectorsReal();
// Printing eigenvalues.
Console.WriteLine("Eigenvalues:");
for (int i = 0; i < nconv; i++)
{
Console.WriteLine(" lambda[" + (i + 1) + "]: " + evals[i]);
}
Console.WriteLine();
if (evecs != null)
{
// Printing the residual norm || A*x - lambda*B*x ||
// for the nconv accurately computed eigenvectors.
var x = new double[n];
var y = new double[n];
var r = new double[nconv]; // residuals
for (int i = 0; i < nconv; i++)
{
var lambda = evals[i];
evecs.Column(i, x);
Vector.Copy(x, y);
// y = B*x
B.Multiply(x, y);
// y = A*x - lambda*B*x
A.Multiply(1.0, x, -lambda, y);
r[i] = Vector.Norm(y) / Math.Abs(lambda);
}
for (int i = 0; i < nconv; i++)
{
Console.WriteLine("||A*x(" + i + ") - lambda(" + i + ")*B*x(" + i + ")||: " + r[i]);
}
Console.WriteLine();
}
}
