public static double[] eigs(EigenDecomposition<DenseMatrix64F> decomp) {
double[] togo = new double[decomp.getNumberOfEigenvalues()];
for (int i = 0; i < togo.length; ++i) {
togo[i] = decomp.getEigenvalue(i).real;
}
return togo;
}
public static OptimisationReport NiuDunLaFuSunStep(
MatrixChains chain, double[] angles, boolean printStep) {
// see: http://code.google.com/p/efficient-java-matrix-library/wiki/SolvingLinearSystems
int l = angles.length;
DenseMatrix64F J = get2d(chain, angles);
DenseMatrix64F grad = get1d(chain, angles);
DenseMatrix64F propose = vector(l);
boolean solved = CommonOps.solve(J, grad, propose);
for (int i = 0; i < l; ++i) {
if (Double.isNaN(propose.data[i])) {
solved = false;
}
}
OptimisationReport togo = new OptimisationReport();
try {
if (!solved) {
throw new RuntimeException("Failed to solve update equation");
}
CommonOps.scale(-1, propose);
if (printStep) {
EigenDecomposition<DenseMatrix64F> decomp = DecompositionFactory.eigSymm(l, false);
boolean posed = decomp.decompose(J);
if (!posed) {
throw new RuntimeException("Failed to decompose matrix");
}
double[] eigs = eigs(decomp);
System.out.println("Computed eigenvalues " + printVec(eigs));
togo.eigenvalues = eigs;
}
tryProposal(chain, propose, grad, angles, printStep, togo);
} catch (Exception e) {
System.out.println(
"Failed to find suitable proposal from Newton step - trying gradient step");
propose.set(grad);
CommonOps.scale(-1, propose);
tryProposal(chain, propose, grad, angles, printStep, togo);
}
return togo;
}
