Source code for pygsl.roots

#!/usr/bin/python3
# Author : Pierre Schnizer 
"""
Wrapper over the functions as described in Chapter 31 of the
reference manual.

Routines for finding the root of a function of one variable.

Example: searching the root of a quadratic using brent:

def quadratic(x, params):
    a = params[0]
    b = params[1]
    c = params[2]
    return  a * x ** 2 + b * x + c

a = 1.0
b = 0.0
c = -5.0
sys = gsl_function(quadratic, (a,b,c))
solver = brent(sys)
while 1:
            iter += 1
            status = solver.iterate()
            x_lo = solver.x_lower()
            x_up = solver.x_upper()
            status = roots.test_interval(x_lo, x_up, 0, 0.001)
            r = solver.root()
            if status == 0:
                break
print "Root Found =", root

"""
from . import _callback

from .gsl_function import gsl_function_fdf, gsl_function
from ._generic_solver import _generic_solver

[docs] class _fsolver(_generic_solver): type = None _alloc = _callback.gsl_root_fsolver_alloc _free = _callback.gsl_root_fsolver_free _set = _callback.gsl_root_fsolver_set _name = _callback.gsl_root_fsolver_name _iterate = _callback.gsl_root_fsolver_iterate _root = _callback.gsl_root_fsolver_root
[docs] def set(self, x_lower, x_upper): """ Set the bondary for the solver. input : x_lower, x_upper x_lower : the lower bound x_upper : the upper bound """ f = self.system.get_ptr() self._set(self._ptr, f, x_lower, x_upper) self._isset = 1
[docs] def root(self): """ Get the actual guess for the root """ return self._root(self._ptr)
[docs] def x_lower(self): """ Get the lower bound of the actual interval """ return _callback.gsl_root_fsolver_x_lower(self._ptr)
[docs] def x_upper(self): """ Get the upper bound of the actual interval """ return _callback.gsl_root_fsolver_x_upper(self._ptr)
class _fdfsolver(_fsolver): type = None _alloc = _callback.gsl_root_fdfsolver_alloc _free = _callback.gsl_root_fdfsolver_free _set = _callback.gsl_root_fdfsolver_set _name = _callback.gsl_root_fdfsolver_name _iterate = _callback.gsl_root_fdfsolver_iterate _root = _callback.gsl_root_fdfsolver_root def set(self, x): """ Set the initial start guess for the solver. input : x x : start value """ f = self.system.get_ptr() self._set(self._ptr, f, x) self._isset = 1
[docs] def test_interval(x_lower, x_upper, eps_abs, eps_rel): r""" This function tests for the convergence of the interval [X_LOWER, X_UPPER] with absolute error EPSABS and relative error EPSREL. The test returns `GSL_SUCCESS' if the following condition is achieved, |a - b| < epsabs + epsrel min(|a|,|b|) when the interval x = [a,b] does not include the origin. If the interval includes the origin then \min(|a|,|b|) is replaced by zero (which is the minimum value of |x| over the interval). This ensures that the relative error is accurately estimated for roots close to the origin. This condition on the interval also implies that any estimate of the root r in the interval satisfies the same condition with respect to the true root r^*, |r - r^*| < epsabs + epsrel r^* assuming that the true root r^* is contained within the interval. input : x_lower, x_upper, eps_abs, eps_rel """ return _callback.gsl_root_test_interval(x_lower, x_upper, eps_abs, eps_rel)
[docs] def test_delta(x_lower, x_upper, eps_abs, eps_rel): """ his function tests the residual value F against the absolute error bound EPSABS. The test returns `GSL_SUCCESS' if the following condition is achieved, |f| < epsabs and returns `GSL_CONTINUE' otherwise. This criterion is suitable for situations where the precise location of the root, x, is unimportant provided a value can be found where the residual, |f(x)|, is small enough. input : x_lower, x_upper, eps_abs, eps_rel """ return _callback.gsl_root_test_delta(x_lower, x_upper, eps_abs, eps_rel)
[docs] class bisection(_fsolver): """ The "bisection algorithm" is the simplest method of bracketing the roots of a function. It is the slowest algorithm provided by the library, with linear convergence. On each iteration, the interval is bisected and the value of the function at the midpoint is calculated. The sign of this value is used to determine which half of the interval does not contain a root. That half is discarded to give a new, smaller interval containing the root. This procedure can be continued indefinitely until the interval is sufficiently small. At any time the current estimate of the root is taken as the midpoint of the interval. """ type = _callback.cvar.gsl_root_fsolver_bisection
[docs] class brent(_fsolver): """ The "Brent-Dekker method" (referred to here as "Brent's method") combines an interpolation strategy with the bisection algorithm. This produces a fast algorithm which is still robust. On each iteration Brent's method approximates the function using an interpolating curve. On the first iteration this is a linear interpolation of the two endpoints. For subsequent iterations the algorithm uses an inverse quadratic fit to the last three points, for higher accuracy. The intercept of the interpolating curve with the x-axis is taken as a guess for the root. If it lies within the bounds of the current interval then the interpolating point is accepted, and used to generate a smaller interval. If the interpolating point is not accepted then the algorithm falls back to an ordinary bisection step. The best estimate of the root is taken from the most recent interpolation or bisection. """ type = _callback.cvar.gsl_root_fsolver_brent
[docs] class falsepos(_fsolver): """ The "false position algorithm" is a method of finding roots based on linear interpolation. Its convergence is linear, but it is usually faster than bisection. On each iteration a line is drawn between the endpoints (a,f(a)) and (b,f(b)) and the point where this line crosses the x-axis taken as a "midpoint". The value of the function at this point is calculated and its sign is used to determine which side of the interval does not contain a root. That side is discarded to give a new, smaller interval containing the root. This procedure can be continued indefinitely until the interval is sufficiently small. The best estimate of the root is taken from the linear interpolation of the interval on the current iteration. """ type = _callback.cvar.gsl_root_fsolver_falsepos
[docs] class newton(_fdfsolver): """ Newton's Method is the standard root-polishing algorithm. The algorithm begins with an initial guess for the location of the root. On each iteration, a line tangent to the function f is drawn at that position. The point where this line crosses the x-axis becomes the new guess. The iteration is defined by the following sequence, x_{i+1} = x_i - f(x_i)/f'(x_i) Newton's method converges quadratically for single roots, and linearly for multiple roots. """ type = _callback.cvar.gsl_root_fdfsolver_newton
[docs] class secant(_fdfsolver): r""" The "secant method" is a simplified version of Newton's method does not require the computation of the derivative on every step. On its first iteration the algorithm begins with Newton's method, using the derivative to compute a first step, x_1 = x_0 - f(x_0)/f'(x_0) Subsequent iterations avoid the evaluation of the derivative by replacing it with a numerical estimate, the slope through the previous two points, x_{i+1} = x_i f(x_i) / f'_{est} where f'_{est} = (f(x_i) - f(x_{i-1})/(x_i - x_{i-1}) When the derivative does not change significantly in the vicinity of the root the secant method gives a useful saving. Asymptotically the secant method is faster than Newton's method whenever the cost of evaluating the derivative is more than 0.44 times the cost of evaluating the function itself. As with all methods of computing a numerical derivative the estimate can suffer from cancellation errors if the separation of the points becomes too small. On single roots, the method has a convergence of order (1 + \sqrt 5)/2 (approximately 1.62). It converges linearly for multiple roots. """ type = _callback.cvar.gsl_root_fdfsolver_secant
[docs] class steffenson(_fdfsolver): """ The "Steffenson Method" provides the fastest convergence of all the routines. It combines the basic Newton algorithm with an Aitken "delta-squared" acceleration. If the Newton iterates are x_i then the acceleration procedure generates a new sequence R_i, R_i = x_i - (x_{i+1} - x_i)^2 / (x_{i+2} - 2 x_{i+1} + x_{i}) which converges faster than the original sequence under reasonable conditions. The new sequence requires three terms before it can produce its first value so the method returns accelerated values on the second and subsequent iterations. On the first iteration it returns the ordinary Newton estimate. The Newton iterate is also returned if the denominator of the acceleration term ever becomes zero. As with all acceleration procedures this method can become unstable if the function is not well-behaved. """ type = _callback.cvar.gsl_root_fdfsolver_steffenson