Interface BiconjugateGradient


public interface BiconjugateGradient
The biconjugate gradient method to solve systems of linear equations.
  • Field Summary

    Fields
    Modifier and Type
    Field
    Description
    static final org.slf4j.Logger
     
  • Method Summary

    Static Methods
    Modifier and Type
    Method
    Description
    static double
    Solves A * x = b by iterative biconjugate gradient method with Jacobi preconditioner matrix.
    static double
    solve(Matrix A, Vector b, Vector x, Preconditioner P, double tol, int itol, int maxIter)
    Solves A * x = b by iterative biconjugate gradient method.
  • Field Details

    • logger

      static final org.slf4j.Logger logger
  • Method Details

    • solve

      static double solve(Matrix A, Vector b, Vector x)
      Solves A * x = b by iterative biconjugate gradient method with Jacobi preconditioner matrix.
      Parameters:
      A - the linear system.
      b - the right hand side of linear equations.
      x - on input, x should be set to an initial guess of the solution (or all zeros). On output, x is set to the improved solution.
      Returns:
      the estimated error.
    • solve

      static double solve(Matrix A, Vector b, Vector x, Preconditioner P, double tol, int itol, int maxIter)
      Solves A * x = b by iterative biconjugate gradient method.
      Parameters:
      A - the linear system.
      b - the right hand side of linear equations.
      x - on input, x should be set to an initial guess of the solution (or all zeros). On output, x is set to the improved solution.
      P - The preconditioner matrix.
      tol - The desired convergence tolerance.
      itol - Which convergence test is applied. If itol = 1, iteration stops when |Ax - b| / |b| is less than the parameter tolerance. If itol = 2, the stop criterion is that |A-1 (Ax - b)| / |A-1b| is less than tolerance. If tol = 3, |xk+1 - xk|2 is less than tolerance. The setting of tol = 4 is same as tol = 3 except that the L norm instead of L2.
      maxIter - The maximum number of iterations.
      Returns:
      the estimated error.