hinsberg_optimization_4 Namespace Reference

Classes
class	K_B
class	K_component
class	K_sum
class	Functional

Functions
def	DawsonIntegral (x)
def	FindZero (f, x0)
def	ApproximateQuadrature (times, f)
def	SubstituteRichardsons (approx_successive_values, k, order, level=- 1)
def	FillUpMatrices (F, ais, tis)
def	GetExponentialsCoefficients (functional, a0, t0)
def	TimesToTaus (times)
def	TausToTimes (taus)

Variables
list	tis = [0.1, 0.3, 1., 3., 5.,10., 40., 190., 1000., 6500., 50000.]
list	a0 = [0.4 for t in tis]
int	m = 5
int	tol = 1e-9
int	tol_residual = 1e-6
int	max_iter = 30
bool	still_changing = True
	a = np.array(a0 + tis)
	a_old = np.array(a0 + tis)
	a_best = np.array(a0 + tis)
int	iteration = 0
	F = Functional()
	mod_residual = F.Fmod()
	best_residual = F.F()
	old_residual = best_residual
float	gamma_0 = 0.5
	grad
	H_inv
	p = H_inv.dot(grad)
	residual = F.F()
	gradient_norm = sum([abs(float(g)) for g in grad])

Function Documentation

ApproximateQuadrature()

def hinsberg_optimization_4.ApproximateQuadrature	(		times,
			f
	)

DawsonIntegral()

def hinsberg_optimization_4.DawsonIntegral

(

x

)

FillUpMatrices()

def hinsberg_optimization_4.FillUpMatrices	(		F,
			ais,
			tis
	)

FindZero()

def hinsberg_optimization_4.FindZero	(		f,
			x0
	)

GetExponentialsCoefficients()

def hinsberg_optimization_4.GetExponentialsCoefficients	(		functional,
			a0,
			t0
	)

SubstituteRichardsons()

def hinsberg_optimization_4.SubstituteRichardsons	(		approx_successive_values,
			k,
			order,
			level = - 1
	)

TausToTimes()

def hinsberg_optimization_4.TausToTimes

(

taus

)

TimesToTaus()

def hinsberg_optimization_4.TimesToTaus

(

times

)

Variable Documentation

a

hinsberg_optimization_4.a = np.array(a0 + tis)

a0

list hinsberg_optimization_4.a0 = [0.4 for t in tis]

a_best

hinsberg_optimization_4.a_best = np.array(a0 + tis)

a_old

hinsberg_optimization_4.a_old = np.array(a0 + tis)

best_residual

hinsberg_optimization_4.best_residual = F.F()

F

hinsberg_optimization_4.F = Functional()

gamma_0

float hinsberg_optimization_4.gamma_0 = 0.5

grad

hinsberg_optimization_4.grad

gradient_norm

hinsberg_optimization_4.gradient_norm = sum([abs(float(g)) for g in grad])

H_inv

hinsberg_optimization_4.H_inv

iteration

int hinsberg_optimization_4.iteration = 0

m

int hinsberg_optimization_4.m = 5

max_iter

int hinsberg_optimization_4.max_iter = 30

mod_residual

hinsberg_optimization_4.mod_residual = F.Fmod()

old_residual

hinsberg_optimization_4.old_residual = best_residual

p

hinsberg_optimization_4.p = H_inv.dot(grad)

residual

hinsberg_optimization_4.residual = F.F()

still_changing

int hinsberg_optimization_4.still_changing = True

tis

def hinsberg_optimization_4.tis = [0.1, 0.3, 1., 3., 5.,10., 40., 190., 1000., 6500., 50000.]

tol

int hinsberg_optimization_4.tol = 1e-9

tol_residual

int hinsberg_optimization_4.tol_residual = 1e-6