SG++-Doxygen-Documentation
python.painlesscg Namespace Reference

## Functions

def ApplyA (B, C, alpha, result, x, l)

def BiCGStab (b, alpha, imax, epsilon, ApplyMatrix, verbose=True)

def cg (y, alpha, grid, x, imax, epsilon, l, verbose=True)

def cg_new (b, alpha, imax, epsilon, ApplyMatrix, reuse=False, verbose=True, max_threshold=None)
Conjugated Gradient method for sparse grids, solving A.alpha=b. More...

def sd (y, alpha, grid, x, imax, epsilon, l)

## Function Documentation

 def python.painlesscg.ApplyA ( B, C, alpha, result, x, l )
 def python.painlesscg.BiCGStab ( b, alpha, imax, epsilon, ApplyMatrix, verbose = True )
 def python.painlesscg.cg ( y, alpha, grid, x, imax, epsilon, l, verbose = True )
 def python.painlesscg.cg_new ( b, alpha, imax, epsilon, ApplyMatrix, reuse = False, verbose = True, max_threshold = None )

Conjugated Gradient method for sparse grids, solving A.alpha=b.

The resulting vector is stored in alpha.

Parameters
 b RHS of equation alpha vector of unknowns imax max. number of iterations (abort, if reached) epsilon accuracy requirements (reduce initial norm of residuum |delta_0| below epsilon*|delta_0|) ApplyMatrix procedure that applies A to a vector reuse starting vector is 0 by default. If true, use current values in alpha verbose verbose output (default False) max_threshold maximal threshold
Returns
tuple (number of iterations, final norm of residuum)
 def python.painlesscg.sd ( y, alpha, grid, x, imax, epsilon, l )