Class CholeskyOuterSolver_MT_DDRB

All Implemented Interfaces:
LinearSolver<DMatrixRBlock,DMatrixRBlock>, LinearSolverDense<DMatrixRBlock>

@Generated("org.ejml.dense.block.linsol.chol.CholeskyOuterSolver_DDRB") public class CholeskyOuterSolver_MT_DDRB extends Object implements LinearSolverDense<DMatrixRBlock>

Linear solver that uses a block cholesky decomposition.

Solver works by using the standard Cholesky solving strategy:
L*LT*x = b
L*y = b
LT*x = y
x = L-Ty

It is also possible to use the upper triangular cholesky decomposition.

  • Constructor Details

    • CholeskyOuterSolver_MT_DDRB

      public CholeskyOuterSolver_MT_DDRB()
  • Method Details

    • setA

      public boolean setA(DMatrixRBlock A)
      Decomposes and overwrites the input matrix.
      Specified by:
      setA in interface LinearSolver<DMatrixRBlock,DMatrixRBlock>
      A - Semi-Positive Definite (SPD) system matrix. Modified. Reference saved.
      If the matrix can be decomposed. Will always return false of not SPD.
    • 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.

      Specified by:
      quality in interface LinearSolver<DMatrixRBlock,DMatrixRBlock>
      The quality of the linear system.
    • solve

      public void solve(DMatrixRBlock B, @Nullable @Nullable DMatrixRBlock X)
      If X == null then the solution is written into B. Otherwise the solution is copied from B into X.
      Specified by:
      solve in interface LinearSolver<DMatrixRBlock,DMatrixRBlock>
      B - A matrix ℜ m × p. Might be modified.
      X - A matrix ℜ n × p, where the solution is written to. Modified.
    • invert

      public void invert(DMatrixRBlock A_inv)
      Description copied from interface: LinearSolverDense
      Computes the inverse of of the 'A' matrix passed into LinearSolver.setA(Matrix) and writes the results to the provided matrix. If 'A_inv' needs to be different from 'A' is implementation dependent.
      Specified by:
      invert in interface LinearSolverDense<DMatrixRBlock>
      A_inv - Where the inverted matrix saved. Modified.
    • modifiesA

      public boolean modifiesA()
      Description copied from interface: LinearSolver
      Returns true if the passed in matrix to LinearSolver.setA(Matrix) is modified.
      Specified by:
      modifiesA in interface LinearSolver<DMatrixRBlock,DMatrixRBlock>
      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.
      Specified by:
      modifiesB in interface LinearSolver<DMatrixRBlock,DMatrixRBlock>
      true if B is modified in solve(B,X).
    • getDecomposition

      public CholeskyDecomposition_F64<DMatrixRBlock> 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.
      Specified by:
      getDecomposition in interface LinearSolver<DMatrixRBlock,DMatrixRBlock>
      Internal decomposition class. If there is none then null.