Package org.ejml.dense.row.linsol.qr

Class LinearSolverQrHouseCol_FDRM

java.lang.Object
org.ejml.dense.row.linsol.LinearSolverAbstract_FDRM
org.ejml.dense.row.linsol.qr.LinearSolverQrHouseCol_FDRM
All Implemented Interfaces:
LinearSolver<FMatrixRMaj,FMatrixRMaj>, LinearSolverDense<FMatrixRMaj>
Direct Known Subclasses:
LinearSolverQrHouseCol_MT_FDRM
@Generated("org.ejml.dense.row.linsol.qr.LinearSolverQrHouseCol_DDRM") public class LinearSolverQrHouseCol_FDRM extends LinearSolverAbstract_FDRM

QR decomposition can be used to solve for systems. However, this is not as computationally efficient as LU decomposition and costs about 3n2 flops.

It solve for x by first multiplying b by the transpose of Q then solving for the result.
QRx=b
Rx=Q^T b

A column major decomposition is used in this solver.

  • Field Details

  • Constructor Details

    • LinearSolverQrHouseCol_FDRM

      public LinearSolverQrHouseCol_FDRM()
      Creates a linear solver that uses QR decomposition.

    • LinearSolverQrHouseCol_FDRM

      protected LinearSolverQrHouseCol_FDRM(QRDecompositionHouseholderColumn_FDRM decomposer)

  • Method Details

    • setMaxSize

      public void setMaxSize(int maxRows, int maxCols)

    • setA

      public boolean setA(FMatrixRMaj A)
      Performs QR decomposition on A
      Parameters:
      A - not modified.
      Returns:
      true if it can be processed.

    • quality

      public double quality()
      Description copied from interface: LinearSolver

      Returns a very quick to compute measure of how singular the system is. This measure will be invariant to the scale of the matrix and always be positive, with larger values indicating it is less singular. If not supported by the solver then the runtime exception IllegalArgumentException is thrown. This is NOT the matrix's condition.

      How this function is implemented is not specified. One possible implementation is the following: In many decompositions a triangular matrix is extracted. The determinant of a triangular matrix is easily computed and once normalized to be scale invariant and its absolute value taken it will provide functionality described above.

      Returns:
      The quality of the linear system.

    • solve

      public void solve(FMatrixRMaj B, FMatrixRMaj X)
      Solves for X using the QR decomposition.
      Parameters:
      B - A matrix that is n by m. Not modified.
      X - An n by m matrix where the solution is written to. Modified.

    • modifiesA

      public boolean modifiesA()
      Description copied from interface: LinearSolver
      Returns true if the passed in matrix to LinearSolver.setA(Matrix) is modified.
      Returns:
      true if A is modified in setA().

    • modifiesB

      public boolean modifiesB()
      Description copied from interface: LinearSolver
      Returns true if the passed in 'B' matrix to LinearSolver.solve(Matrix, Matrix) is modified.
      Returns:
      true if B is modified in solve(B,X).

    • getDecomposition

      public QRDecomposition<FMatrixRMaj> getDecomposition()
      Description copied from interface: LinearSolver
      If a decomposition class was used internally then this will return that class. Most linear solvers decompose the input matrix into a more simplistic form. However some solutions do not require decomposition, e.g. inverse by minor.
      Returns:
      Internal decomposition class. If there is none then null.