Package org.ejml.dense.row.linsol.qr

Class LinearSolverQr_CDRM

java.lang.Object
org.ejml.dense.row.linsol.LinearSolverAbstract_CDRM
org.ejml.dense.row.linsol.qr.LinearSolverQr_CDRM
All Implemented Interfaces:
LinearSolver<CMatrixRMaj,CMatrixRMaj>, LinearSolverDense<CMatrixRMaj>
@Generated("org.ejml.dense.row.linsol.qr.LinearSolverQr_ZDRM") public class LinearSolverQr_CDRM extends LinearSolverAbstract_CDRM

A solver for a generic QR decomposition algorithm. This will in general be a bit slower than the specialized once since the full Q and R matrices need to be extracted.

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

  • Field Details

  • Constructor Details

    • LinearSolverQr_CDRM

      public LinearSolverQr_CDRM(QRDecomposition<CMatrixRMaj> decomposer)
      Creates a linear solver that uses QR decomposition.

  • Method Details

    • setMaxSize

      public void setMaxSize(int maxRows, int maxCols)
      Changes the size of the matrix it can solve for
      Parameters:
      maxRows - Maximum number of rows in the matrix it will decompose.
      maxCols - Maximum number of columns in the matrix it will decompose.

    • setA

      public boolean setA(CMatrixRMaj 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(CMatrixRMaj B, CMatrixRMaj 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<CMatrixRMaj> 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.

    • getDecomposer

      public QRDecomposition<CMatrixRMaj> getDecomposer()

    • getQ

      public CMatrixRMaj getQ()

    • getR

      public CMatrixRMaj getR()