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

ApplyA()

def python.painlesscg.ApplyA	(		B,
			C,
			alpha,
			result,
			x,
			l
	)

Referenced by python.painlesscg.cg(), and python.painlesscg.sd().

BiCGStab()

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

Referenced by sgpp::datadriven::ModelFittingBase.buildSolver().

cg()

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

References python.painlesscg.ApplyA().

cg_new()

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)

Referenced by python.classifier.doTest(), python.classifier.performFold(), python.classifier.performFoldNew(), python.classifier.performFoldRegression(), and python.classifier.run().

sd()

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

References python.painlesscg.ApplyA().

Referenced by sgpp::base::HashGenerator.regular_boundary_truncated_iter(), and sgpp::base::HashGenerator.regular_periodic_boundary_iter().