Package org.ejml.dense.row.linsol.chol

Class LinearSolverCholLDL_FDRM

java.lang.Object
org.ejml.dense.row.linsol.LinearSolverAbstract_FDRM
org.ejml.dense.row.linsol.chol.LinearSolverCholLDL_FDRM
All Implemented Interfaces:
LinearSolver<FMatrixRMaj,FMatrixRMaj>, LinearSolverDense<FMatrixRMaj>
@Generated("org.ejml.dense.row.linsol.chol.LinearSolverCholLDL_DDRM") public class LinearSolverCholLDL_FDRM extends LinearSolverAbstract_FDRM

  • Constructor Details

    • LinearSolverCholLDL_FDRM

      public LinearSolverCholLDL_FDRM(CholeskyDecompositionLDL_FDRM decomposer)

    • LinearSolverCholLDL_FDRM

      public LinearSolverCholLDL_FDRM()

  • Method Details

    • setA

      public boolean setA(FMatrixRMaj A)
      Description copied from interface: LinearSolver

      Specifies the A matrix in the linear equation. A reference might be saved and it might also be modified depending on the implementation. If it is modified then LinearSolver.modifiesA() will return true.

      If this value returns true that does not guarantee a valid solution was generated. This is because some decompositions don't detect singular matrices.

      Parameters:
      A - The 'A' matrix in the linear equation. Might be modified or save the reference.
      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)

      Using the decomposition, finds the value of 'X' in the linear equation below:
      A*x = b
      where A has dimension of n by n, x and b are n by m dimension.

      *Note* that 'b' and 'x' can be the same matrix instance.

      Parameters:
      B - A matrix that is n by m. Not modified.
      X - An n by m matrix where the solution is writen to. Modified.

    • invert

      public void invert(FMatrixRMaj inv)
      Sets the matrix 'inv' equal to the inverse of the matrix that was decomposed.
      Specified by:
      invert in interface LinearSolverDense<FMatrixRMaj>
      Overrides:
      invert in class LinearSolverAbstract_FDRM
      Parameters:
      inv - Where the value of the inverse will be stored. 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 CholeskyLDLDecomposition_F32<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.