/**
* <p>
* Linear solver which uses QR pivot decomposition. These solvers can handle singular systems
* and should never fail. For singular systems, the solution might not be as accurate as a
* pseudo inverse that uses SVD.
* </p>
*
* <p>
* For singular systems there are multiple correct solutions. The optimal 2-norm solution is the
* solution vector with the minimal 2-norm and is unique. If the optimal solution is not computed
* then the basic solution is returned. See {@link org.ejml.dense.row.linsol.qr.BaseLinearSolverQrp_FDRM}
* for details. There is only a runtime difference for small matrices, 2-norm solution is slower.
* </p>
*
* <p>
* Two different solvers are available. Compute Q will compute the Q matrix once then use it multiple times.
* If the solution for a single vector is being found then this should be set to false. If the pseudo inverse
* is being found or the solution matrix has more than one columns AND solve is being called numerous multiples
* times then this should be set to true.
* </p>
*
* @param computeNorm2 true to compute the minimum 2-norm solution for singular systems. Try true.
* @param computeQ Should it precompute Q or use house holder. Try false;
* @return Pseudo inverse type solver using QR with column pivots.
*/
public static LinearSolverDense <FMatrixRMaj> leastSquaresQrPivot(bool computeNorm2, bool computeQ)
{
QRColPivDecompositionHouseholderColumn_FDRM decomposition =
new QRColPivDecompositionHouseholderColumn_FDRM();
if (computeQ)
{
return(new SolvePseudoInverseQrp_FDRM(decomposition, computeNorm2));
}
else
{
return(new LinearSolverQrpHouseCol_FDRM(decomposition, computeNorm2));
}
}
public LinearSolverQrpHouseCol_FDRM(QRColPivDecompositionHouseholderColumn_FDRM decomposition,
bool norm2Solution)
: base(decomposition, norm2Solution)
{
this.decomposition = decomposition;
}
