/// <summary>
/// Finds a vector argument which makes a vector function zero.
/// </summary>
/// <param name="f">The vector function.</param>
/// <param name="x0">The vector argument.</param>
/// <returns>The vector argument which makes all components of the vector function zero.</returns>
/// <exception cref="ArgumentNullException"><paramref name="f"/> or <paramref name="x0"/> is null.</exception>
/// <exception cref="DimensionMismatchException">The dimension of <paramref name="f"/> is not equal to the
/// dimension of <paramref name="x0"/>.</exception>
public static ColumnVector FindZero(Func <IList <double>, IList <double> > f, IList <double> x0)
{
if (f == null)
{
throw new ArgumentNullException("f");
}
if (x0 == null)
{
throw new ArgumentNullException("x0");
}
// we will use Broyden's method, which is a generalization of the secant method to the multi-dimensional problem
// just as the secant method is essentially Newton's method with a crude, numerical value for the slope,
// Broyden's method is a multi-dimensional Newton's method with a crude, numerical value for the Jacobian matrix
int d = x0.Count;
// we should re-engineer this code to work directly on vector/matrix storage rather than go back and forth
// between vectors and arrays, but for the moment this works; implementing Blas2 rank-1 update would help
// starting values
ColumnVector x = new ColumnVector(x0);
//double[] x = new double[d]; Blas1.dCopy(x0, 0, 1, x, 0, 1, d);
ColumnVector F = new ColumnVector(f(x.ToArray()));
//double[] F = f(x);
if (F.Dimension != d)
{
throw new DimensionMismatchException();
}
SquareMatrix B = ApproximateJacobian(f, x0);
double g = MoreMath.Pow2(F.Norm());
//double g = Blas1.dDot(F, 0, 1, F, 0, 1, d);
for (int n = 0; n < Global.SeriesMax; n++)
{
// determine the Newton step
LUDecomposition LU = B.LUDecomposition();
ColumnVector dx = -LU.Solve(F);
//for (int i = 0; i < d; i++) {
// Console.WriteLine("F[{0}]={1} x[{0}]={2} dx[{0}]={3}", i, F[i], x[i], dx[i]);
//}
// determine how far we will move along the Newton step
ColumnVector x1 = x + dx;
ColumnVector F1 = new ColumnVector(f(x1));
double g1 = MoreMath.Pow2(F1.Norm());
// check whether the Newton step decreases the function vector magnitude
// NR suggest that it's necessary to ensure that it decrease by a certain amount, but I have yet to see that make a diference
double gm = g - 0.0;
if (g1 > gm)
{
// the Newton step did not reduce the function vector magnitude, so we won't step that far
// determine how far along the descent direction we will step by parabolic interpolation
double z = g / (g + g1);
//Console.WriteLine("z={0}", z);
// take at least a small step in the descent direction
if (z < (1.0 / 16.0))
{
z = 1.0 / 16.0;
}
dx = z * dx;
x1 = x + dx;
F1 = new ColumnVector(f(x1.ToArray()));
g1 = MoreMath.Pow2(F1.Norm());
// NR suggest that this be repeated, but I have yet to see that make a difference
}
// take the step
x = x + dx;
// check for convergence
if ((F1.InfinityNorm() < Global.Accuracy) || (dx.InfinityNorm() < Global.Accuracy))
{
//if ((g1 < Global.Accuracy) || (dx.Norm() < Global.Accuracy)) {
return(x);
}
// update B
ColumnVector dF = F1 - F;
RectangularMatrix dB = (dF - B * dx) * (dx / (dx.Transpose() * dx)).Transpose();
//RectangularMatrix dB = F1 * ( dx / MoreMath.Pow(dx.Norm(), 2) ).Transpose();
for (int i = 0; i < d; i++)
{
for (int j = 0; j < d; j++)
{
B[i, j] += dB[i, j];
}
}
// prepare for the next iteration
F = F1;
g = g1;
}
throw new NonconvergenceException();
}
