m_n_kappa.solver.Newton

class m_n_kappa.solver.Newton(data, target, variable)

Bases: Solver

Solver using the newton method

New in version 0.1.0.

Parameters:

• data (list[dict] | list[list]) – data containing target and variable keys

• target (str | int) – key of the target (e.g. str for dictionaries or int for lists)

• variable (str | int) – variable of the target (e.g. str for dictionaries or int for lists)

Methods

compute([use_fallback])

compute a new value using the newton algorithm

Attributes

data	passed data
function	function to compute x_n_plus_1
maximum_variable	maximum variable value given in data
minimum_variable	minimum variable value given in data
target	key of the target in data
variable	key of the variable in data
x_n	lastly computed variable value
x_n_plus_1	new computed value

compute(use_fallback=False)

compute a new value using the newton algorithm

In case the newton algorithm does not lead to an optimization of the variable value then bi-section will be used as fallback. Optimization means improvement of variable-value leading to a target-value nearer zero.

Parameters:

use_fallback (bool) – use the fallback algorithm (i.e. Bisection)

Returns:

computed new value leading to target-value nearer zero

Return type:

float

See also

Bisection

Solver-class using bi-sectional approach

property data: list

passed data

property function

function to compute x_n_plus_1

property maximum_variable: float

maximum variable value given in data

property minimum_variable: float

minimum variable value given in data

property target: str | int

key of the target in data

property variable: str | int

key of the variable in data

property x_n: float

lastly computed variable value

property x_n_plus_1: float

new computed value