/** * <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; }