/**
* Makes a draw on the distribution. The results are added to parameter 'x'
*/
public void next(DMatrixRMaj x)
{
for (int i = 0; i < r.numRows; i++)
{
r.set(i, 0, (double)rand.NextGaussian());
}
CommonOps_DDRM.multAdd(A, r, x);
}
public override /**/ double quality()
{
return(SpecializedOps_DDRM.qualityTriangular(R));
}
/**
* <p>
* Upgrades the basic solution to the optimal 2-norm solution.
* </p>
*
* <pre>
* First solves for 'z'
*
* || x_b - P*[ R_11^-1 * R_12 ] * z ||2
* min z || [ - I_{n-r} ] ||
*
* </pre>
*
* @param X basic solution, also output solution
*/
protected void upgradeSolution(DMatrixRMaj X)
{
DMatrixRMaj z = Y; // recycle Y
// compute the z which will minimize the 2-norm of X
// because of the identity matrix tacked onto the end 'A' should never be singular
if (!internalSolver.setA(W))
{
throw new InvalidOperationException("This should never happen. Is input NaN?");
}
z.reshape(numCols - rank, 1);
internalSolver.solve(X, z);
// compute X by tweaking the original
CommonOps_DDRM.multAdd(-1, W, z, X);
}