/// <summary>
/// Computes the product of two square matrices.
/// </summary>
/// <param name="A">The first matrix.</param>
/// <param name="B">The second matrix.</param>
/// <returns>The product <paramref name="A"/> * <paramref name="B"/>.</returns>
/// <remarks>
/// <para>Note that matrix multiplication is not commutative, i.e. M1*M2 is generally not the same as M2*M1.</para>
/// <para>Matrix multiplication is an O(N<sup>3</sup>) process.</para>
/// </remarks>
/// <exception cref="ArgumentNullException"><paramref name="A"/> or <paramref name="B"/> is <see langword="null"/>.</exception>
/// <exception cref="DimensionMismatchException">The dimension of <paramref name="A"/> is not the same as the dimension of <paramref name="B"/>.</exception>
public static SquareMatrix operator *(SquareMatrix A, SquareMatrix B)
{
// this is faster than the base operator, because it knows about the underlying structure
if (A == null)
{
throw new ArgumentNullException(nameof(A));
}
if (B == null)
{
throw new ArgumentNullException(nameof(B));
}
if (A.dimension != B.dimension)
{
throw new DimensionMismatchException();
}
double[] abStore = MatrixAlgorithms.Multiply(
A.store, A.offset, A.rowStride, A.colStride,
B.store, B.offset, B.rowStride, B.colStride,
A.dimension, A.dimension, A.dimension
);
return(new SquareMatrix(abStore, A.dimension));
}
/// <summary>
/// Multiplies two real, rectangular matrices.
/// </summary>
/// <param name="A">The first matrix.</param>
/// <param name="B">The second matrix.</param>
/// <returns>The product matrix AB.</returns>
public static RectangularMatrix operator *(RectangularMatrix A, RectangularMatrix B)
{
// This is faster than the base operator, because it knows about the underlying storage structure
if (A == null)
{
throw new ArgumentNullException(nameof(A));
}
if (B == null)
{
throw new ArgumentNullException(nameof(B));
}
if (A.cols != B.rows)
{
throw new DimensionMismatchException();
}
double[] abStore = MatrixAlgorithms.Multiply(
A.store, A.offset, A.rowStride, A.colStride,
B.store, B.offset, B.rowStride, B.colStride,
A.rows, A.cols, B.cols
);
return(new RectangularMatrix(abStore, A.rows, B.cols));
}
/// <summary>
/// Returns the transpose of the matrix.
/// </summary>
/// <returns>M<sup>T</sup></returns>
public RectangularMatrix Transpose()
{
double[] tStore = MatrixAlgorithms.Transpose(store, rows, cols);
return(new RectangularMatrix(tStore, cols, rows));
}
/// <summary>
/// Computes the inverse of the matrix.
/// </summary>
/// <returns>The matrix inverse M<sup>-1</sup>.</returns>
/// <remarks>
/// <para>The inverse of a matrix M is a matrix M<sup>-1</sup> such that M<sup>-1</sup>M = I, where I is the identity matrix.</para>
/// <para>If the matrix is singular, inversion is not possible. In that case, this method will fail with a <see cref="DivideByZeroException"/>.</para>
/// <para>The inversion of a matrix is an O(N<sup>3</sup>) operation.</para>
/// </remarks>
/// <exception cref="DivideByZeroException">The matrix is singular.</exception>
public SquareMatrix Inverse()
{
double[] iStore = MatrixAlgorithms.Copy(store, offset, rowStride, colStride, dimension, dimension);
SquareMatrixAlgorithms.GaussJordanInvert(iStore, dimension);
return(new SquareMatrix(iStore, dimension));
}
/// <summary>
/// Copies the matrix.
/// </summary>
/// <returns>An independent copy of the matrix.</returns>
public SquareMatrix Copy()
{
double[] copyStore = MatrixAlgorithms.Copy(store, offset, rowStride, colStride, dimension, dimension);
return(new SquareMatrix(copyStore, dimension));
}
/// <inheritdoc />
public override double OneNorm()
{
return(MatrixAlgorithms.OneNorm(store, offset, rowStride, colStride, dimension, dimension));
}
// EIGENVALUE ALGORITHMS
//
public static Complex[] ReduceToRealSchurForm(double[] aStore, double[] qStore, int dimension)
{
// keep track of eigenvalues
Complex[] eigenvalues = new Complex[dimension];
// keep track of interations
int count = 0;
// isolate the upper and lower boundaries of the curent subproblem
// p is the upper index, n the lower index
int n = dimension - 1;
while (n >= 0)
{
// move up the matrix from endpoint n, looking for subdiagonal elements negligible compared to the neighboring diagonal elements
// if we find one, that reduces the problem to the submatrix between p and n
int p = n;
while (p > 0)
{
double d = Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, p, p)) + Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, p - 1, p - 1));
double e = Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, p, p - 1));
if (d + e == d)
{
// double f = d * Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, p, p) - MatrixAlgorithms.GetEntry(aStore, dimension, dimension, p - 1, p - 1));
// double g = e * Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, p - 1, p));
// if (f + g == f) {
MatrixAlgorithms.SetEntry(aStore, dimension, dimension, p, p - 1, 0.0);
break;
// }
}
p--;
}
/*
* if (p == n) {
* // one eigenvalue
* // reduce n and re-set count
* } else {
* // compute m, matrix elements
* // reduce n and re-set count
* if (p == m) {
* // two eigenvalues
* } else {
* // do step
* }
* }
*/
//Print(a, dimension, p, n);
// get the (indexes for) the entries in the trailing 2x2 matrix
int m = n - 1;
int ammi = MatrixAlgorithms.GetIndex(dimension, dimension, m, m);
int amni = MatrixAlgorithms.GetIndex(dimension, dimension, m, n);
int anmi = MatrixAlgorithms.GetIndex(dimension, dimension, n, m);
int anni = MatrixAlgorithms.GetIndex(dimension, dimension, n, n);
if (n - p > 1)
{
count++;
if (count > 32)
{
throw new NonconvergenceException();
}
double tr = aStore[ammi] + aStore[anni];
double det = aStore[ammi] * aStore[anni] - aStore[amni] * aStore[anmi];
// ad hoc shift
if ((count == 8) || (count == 16) || (count == 24))
{
double w = Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, n, n - 1)) +
Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, n - 1, n - 2));
tr = 2.0 * w;
det = w * w;
}
FrancisTwoStep(aStore, qStore, dimension, p, n, tr, det);
}
else
{
if (p == n)
{
eigenvalues[n] = aStore[anni];
}
else if (p == m)
{
double sn, cn;
TwoByTwoRealSchur(ref aStore[ammi], ref aStore[amni], ref aStore[anmi], ref aStore[anni], out sn, out cn, out eigenvalues[m], out eigenvalues[n]);
// Multiply A from left by the rotation matrix R
for (int cc = p + 2; cc < dimension; cc++)
{
int i = MatrixAlgorithms.GetIndex(dimension, dimension, p, cc);
int j = MatrixAlgorithms.GetIndex(dimension, dimension, p + 1, cc);
double t = aStore[i];
aStore[i] = cn * t + sn * aStore[j];
aStore[j] = cn * aStore[j] - sn * t;
}
// Multiply A from the right by R^T
for (int rr = 0; rr < p; rr++)
{
int i = MatrixAlgorithms.GetIndex(dimension, dimension, rr, p);
int j = MatrixAlgorithms.GetIndex(dimension, dimension, rr, p + 1);
double t = aStore[i];
aStore[i] = cn * t + sn * aStore[j];
aStore[j] = cn * aStore[j] - sn * t;
}
// Multiply Q^T from the left by R
if (qStore != null)
{
for (int rr = 0; rr < dimension; rr++)
{
int i = MatrixAlgorithms.GetIndex(dimension, dimension, rr, p);
int j = MatrixAlgorithms.GetIndex(dimension, dimension, rr, p + 1);
double t = qStore[i];
qStore[i] = cn * t + sn * qStore[j];
qStore[j] = cn * qStore[j] - sn * t;
}
}
}
n = p - 1;
count = 0;
}
}
return(eigenvalues);
}
// simple specific operations
/// <summary>
/// Copies the matrix.
/// </summary>
/// <returns>An indpendent copy of the matrix.</returns>
public RectangularMatrix Copy()
{
double[] cStore = MatrixAlgorithms.Copy(store, offset, rowStride, colStride, rows, cols);
return(new RectangularMatrix(cStore, rows, cols));
}
/// <summary>
/// Copies the matrix.
/// </summary>
/// <returns>An independent copy of the matrix.</returns>
public SquareMatrix Copy()
{
double[] cStore = MatrixAlgorithms.Copy(store, dimension, dimension);
return(new SquareMatrix(cStore, dimension));
}
/// <inheritdoc />
public override double InfinityNorm()
{
return(MatrixAlgorithms.InfinityNorm(store, 0, 1, dimension, dimension, dimension));
}
/// <inheritdoc />
public override double OneNorm()
{
return(MatrixAlgorithms.OneNorm(store, 0, 1, dimension, dimension, dimension));
}
public static void SchurEigensystem(double[] aStore, int n, out Complex[] eigenvalues, out Complex[][] eigenvectors)
{
eigenvalues = new Complex[n];
eigenvectors = new Complex[n][];
int i = n - 1;
while (i >= 0)
{
// find the eigenvector corresponding to the ith eigenvalue
if ((i == 0) || (MatrixAlgorithms.GetEntry(aStore, n, n, i, i - 1) == 0.0))
{
// We have no sub-diagonal element on the ith row. Since
// (XXXXX) ( X ) ( X )
// (XXXXX) ( X ) ( X )
// ( aXX) ( 1 ) = ( a )
// ( XX) ( 0 ) ( 0 )
// ( XX) ( 0 ) ( 0 )
// a vector with v[i] = 1, v[j > i] = 0, and v[j < i] properly chosen will be an eigenvector.
// the eigenvalue is the ith diagonal element
double e = MatrixAlgorithms.GetEntry(aStore, n, n, i, i);
// we take the ith component of the eigenvector to be one; higher components must be zero
Complex[] v = new Complex[n];
v[i] = 1.0;
CompleteEigenvector(aStore, n, v, e, i, i - 1);
//WriteTransformedEigenvector(i, e, v, n, Q);
eigenvectors[i] = v;
eigenvalues[i] = e;
i--;
}
else
{
// We have a 2x2 sub-block
// ( X X X )
// ( [X X])
// ( [X X])
// Find its eigenvalues
Complex e1, e2;
TwoByTwoEigenvalues(
MatrixAlgorithms.GetEntry(aStore, n, n, i - 1, i - 1),
MatrixAlgorithms.GetEntry(aStore, n, n, i - 1, i),
MatrixAlgorithms.GetEntry(aStore, n, n, i, i - 1),
MatrixAlgorithms.GetEntry(aStore, n, n, i, i),
out e1, out e2
);
//TwoByTwoEigenvalues(a[i - 1, i - 1], a[i - 1, i], a[i, i - 1], a[i, i], out e1, out e2);
// For each eigenvalue, compute the trailing eigenvector components and then the remaining components.
// For the trailing components, since
// (a11 a12) ( v1 ) = \lambda ( v1 )
// (a21 a22) ( v2 ) ( v2 )
// It follows that
// v1 = (\lambda - a22) / a21 * v2
// and we normalize by choosing v2 = 1. Since we are only here if a21 != 0, there is no danger of
// dividing by zero, but there may well be a danger that (\lambda - a22) looses significant
// precision due to cancelation.
Complex[] v = new Complex[n];
v[i] = 1.0;
//v[i - 1] = (e1 - a[i, i]) / a[i, i - 1];
v[i - 1] = (e1 - MatrixAlgorithms.GetEntry(aStore, n, n, i, i)) / MatrixAlgorithms.GetEntry(aStore, n, n, i, i - 1);
CompleteEigenvector(aStore, n, v, e1, i, i - 2);
//WriteTransformedEigenvector(i, e1, v, n, Q);
eigenvectors[i] = v;
eigenvalues[i] = e1;
v = new Complex[n];
v[i] = 1.0;
//v[i - 1] = (e2 - a[i, i]) / a[i, i - 1];
v[i - 1] = (e2 - MatrixAlgorithms.GetEntry(aStore, n, n, i, i)) / MatrixAlgorithms.GetEntry(aStore, n, n, i, i - 1);
CompleteEigenvector(aStore, n, v, e2, i, i - 2);
//WriteTransformedEigenvector(i - 1, e2, v, n, Q);
eigenvectors[i - 1] = v;
eigenvalues[i - 1] = e2;
i -= 2;
}
}
}
// inverts the matrix in place
// the in-place-ness makes this a bit confusing
public static void GaussJordanInvert(double[] store, int dimension)
{
// keep track of row exchanges
int[] ps = new int[dimension];
// iterate over dimensions
for (int k = 0; k < dimension; k++)
{
// look for a pivot in the kth column on any lower row
int p = k;
double q = MatrixAlgorithms.GetEntry(store, dimension, dimension, k, k);
for (int r = k + 1; r < dimension; r++)
{
double s = MatrixAlgorithms.GetEntry(store, dimension, dimension, r, k);
if (Math.Abs(s) > Math.Abs(q))
{
p = r;
q = s;
}
}
ps[k] = p;
// if no non-zero pivot is found, the matrix is singular and cannot be inverted
if (q == 0.0)
{
throw new DivideByZeroException();
}
// if the best pivot was on a lower row, swap it into the kth row
if (p != k)
{
Blas1.dSwap(store, k, dimension, store, p, dimension, dimension);
}
// divide the pivot row by the pivot element, so the diagonal element becomes unity
MatrixAlgorithms.SetEntry(store, dimension, dimension, k, k, 1.0);
Blas1.dScal(1.0 / q, store, k, dimension, dimension);
// add factors to the pivot row to zero all off-diagonal elements in the kth column
for (int r = 0; r < dimension; r++)
{
if (r == k)
{
continue;
}
double a = MatrixAlgorithms.GetEntry(store, dimension, dimension, r, k);
MatrixAlgorithms.SetEntry(store, dimension, dimension, r, k, 0.0);
Blas1.dAxpy(-a, store, k, dimension, store, r, dimension, dimension);
}
}
// unscramble exchanges
for (int k = dimension - 1; k >= 0; k--)
{
int p = ps[k];
if (p != k)
{
Blas1.dSwap(store, dimension * p, 1, store, dimension * k, 1, dimension);
}
}
}
public static Complex[] ExtractEigenvalues(double[] aStore, double[] qStore, int dimension)
{
int count = 0;
// keep track of extracted eigenvalues
Complex[] lambdas = new Complex[dimension];
double sum_old = Double.PositiveInfinity;
int n = dimension - 1;
while (n >= 0)
{
//Write(aStore, dimension, dimension);
// find the lowest decoupled, unreduced block
int a = n;
double sum = 0.0;
while (a > 0)
{
double f = Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a, a)) +
Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a - 1, a - 1));
double e = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a, a - 1);
if ((f + e) == f)
{
MatrixAlgorithms.SetEntry(aStore, dimension, dimension, a, a - 1, 0.0);
break;
}
sum += Math.Abs(e);
a--;
}
/*
* Console.WriteLine("count = {0}", count);
* double[] qt = MatrixAlgorithms.Transpose(qStore, dimension, dimension);
* double[] qa = MatrixAlgorithms.Multiply(qStore, dimension, dimension, aStore, dimension, dimension);
* double[] qaqt = MatrixAlgorithms.Multiply(qa, dimension, dimension, qt, dimension, dimension);
* MatrixAlgorithms.PrintMatrix(qaqt, dimension, dimension);
*/
// check for maximum numbers of iterations without finding an eigenvalue
if (count > 32)
{
throw new NonconvergenceException();
}
// we are working between a and n, and our block is at least 3 wide
// reduce if possible, otherwise do a iteration step
if (a == n)
{
// 1 X 1 block isolated
lambdas[a] = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a, a);
n -= 1;
count = 0;
sum_old = Double.PositiveInfinity;
}
else if (a == (n - 1))
{
// 2 X 2 block isolated
double Aaa = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a, a);
double Aba = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a + 1, a);
double Aab = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a, a + 1);
double Abb = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, a + 1, a + 1);
// eigenvalues are given by the quadratic formula
// e = \frac{a_11 + a22}{2} \pm q
// descriminant q^2 = ( \frac{a_11 - a_22}{2} )^2 + a_21 a_1
double c = (Aaa + Abb) / 2.0;
double d = Aaa * Abb - Aba * Aab;
double q2 = c * c - d;
if (q2 >= 0.0)
{
// eigenvalues are real
double q = Math.Sqrt(q2);
if (c >= 0.0)
{
lambdas[a] = c + q;
}
else
{
lambdas[a] = c - q;
}
// i tried to do this as c + Math.Sign(c) * q, but when c = 0, Math.Sign(c) = 0, not 1
lambdas[a + 1] = d / lambdas[a];
/*
* double sn, cn;
* TwoByTwoSchur(ref Aaa, ref Aab, ref Aba, ref Abb, out sn, out cn);
* MatrixAlgorithms.SetEntry(aStore, dimension, dimension, a, a, Aaa);
* MatrixAlgorithms.SetEntry(aStore, dimension, dimension, a, a + 1, Aab);
* MatrixAlgorithms.SetEntry(aStore, dimension, dimension, a + 1, a, Aba);
* MatrixAlgorithms.SetEntry(aStore, dimension, dimension, a + 1, a + 1, Abb);
*
* // Multiply A from left by the rotation matrix R
* for (int cc = a + 2; cc < dimension; cc++) {
* int i = MatrixAlgorithms.GetIndex(dimension, dimension, a, cc);
* int j = MatrixAlgorithms.GetIndex(dimension, dimension, a + 1, cc);
* double t = aStore[i];
* aStore[i] = cn * t + sn * aStore[j];
* aStore[j] = cn * aStore[j] - sn * t;
* }
* // Multiply A from the right by R^T
* for (int rr = 0; rr < a; rr++) {
* int i = MatrixAlgorithms.GetIndex(dimension, dimension, rr, a);
* int j = MatrixAlgorithms.GetIndex(dimension, dimension, rr, a + 1);
* double t = aStore[i];
* aStore[i] = cn * t + sn * aStore[j];
* aStore[j] = cn * aStore[j] - sn * t;
* }
* // Multiply Q^T from the left by R
* if (qStore != null) {
* for (int cc = 0; cc < dimension; cc++) {
* int i = MatrixAlgorithms.GetIndex(dimension, dimension, a, cc);
* int j = MatrixAlgorithms.GetIndex(dimension, dimension, a + 1, cc);
* double t = qStore[i];
* qStore[i] = cn * t + sn * qStore[j];
* qStore[j] = cn * qStore[j] - sn * t;
* }
* }
*
* lambdas[a] = Aaa;
* lambdas[a + 1] = Abb;
*/
}
else
{
double q = Math.Sqrt(-q2);
lambdas[a] = new Complex(c, q);
lambdas[a + 1] = new Complex(c, -q);
}
n -= 2;
count = 0;
sum_old = Double.PositiveInfinity;
}
else
{
// use the lower-left 2 X 2 matrix to generate an approximate eigenvalue pair
int m = n - 1;
double Amm = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, m, m);
double Amn = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, m, n);
double Anm = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, n, m);
double Ann = MatrixAlgorithms.GetEntry(aStore, dimension, dimension, n, n);
// the trace of this 2 X 2 matrix is the sum of its eigenvalues, and its determinate is their product
double tr = Amm + Ann;
double det = Amm * Ann - Amn * Anm;
// ad hoc shift
if ((count == 8) || (count == 16) || (count == 24))
{
double w = Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, n, n - 1)) +
Math.Abs(MatrixAlgorithms.GetEntry(aStore, dimension, dimension, m, m - 1));
tr = 2.0 * w;
det = w * w;
}
FrancisTwoStep(aStore, qStore, dimension, a, n, tr, det);
sum_old = sum;
count++;
}
}
return(lambdas);
}
/// <inheritdoc />
public override double InfinityNorm()
{
return(MatrixAlgorithms.InfinityNorm(store, offset, rowStride, colStride, rows, cols));
}
/*
* IMatrix IMatrix.Clone () {
* return (Clone());
* }
*/
/// <summary>
/// Creates a transpose of the matrix.
/// </summary>
/// <returns>The matrix transpose M<sup>T</sup>.</returns>
public SquareMatrix Transpose()
{
double[] tStore = MatrixAlgorithms.Transpose(store, dimension, dimension);
return(new SquareMatrix(tStore, dimension));
}
/// <summary>
/// The orthogonal matrix Q.
/// </summary>
/// <returns>The orthogonal matrix Q.</returns>
public SquareMatrix QMatrix()
{
double[] qStore = MatrixAlgorithms.Transpose(qtStore, dimension, dimension);
return(new SquareMatrix(qStore, dimension));
}
/// <summary>
/// Returns the transpose of the matrix.
/// </summary>
/// <returns>M<sup>T</sup></returns>
public RectangularMatrix Transpose()
{
// Just copy with rows <-> columns to get transpose
double[] transpose = MatrixAlgorithms.Copy(store, offset, colStride, rowStride, cols, rows);
return(new RectangularMatrix(transpose, cols, rows));
}
/// <summary>
/// Returns the left transform matrix.
/// </summary>
/// <returns>The matrix U, such that A = U S V<sup>T</sup>.</returns>
public SquareMatrix LeftTransformMatrix()
{
double[] left = MatrixAlgorithms.Transpose(utStore, rows, rows);
return(new SquareMatrix(left, rows));
}
