Pure python single variable function solvers
A Python library implementing various root finding methods for single-variable functions.
Currently the following methods have been implemented:
With regard to
Brent's method, there are two implementations, the
first one uses inverse quadratic extrapolation (
Brentq) while the
other ones uses hyperbolic extrapolation (
If you don't know which method to use, you should probably use
That being said,
Bisect method is safe and slow (i.e. lots of iterations).
# define the function whose root you are searching def f(x, a): return x ** 2 - a + 1 # Create the Solver object (instead of Brentq you could also import Brenth/Ridder/Bisect) from pyroots import Brentq brent = Brentq(epsilon=1e-5) # solve the function in `[-3, 0]` while `a` is equal to 2 result = brent(f, -3, 0, a=2) print(result)
converged : True message : Solution converged. iterations : 6 func calls : 9 x0 : -1.0000000748530762 xtol : 0.0000000000000002 f(x0) : 0.0000001497061579 epsilon : 0.0000100000000000 x_steps : [-3, 0, -0.3333333333333333, -1.6666666666666665, -0.7777777777777779, -1.0686868686868687, -0.9917335278385606, -0.9997244260982788, -1.0000000748530762] fx_steps : [8, -1, -0.8888888888888888, 1.7777777777777772, -0.3950617283950615, 0.14209162330374459, -0.01646460976088293, -0.0005510718624670563, 1.4970615791476405e-07]
The functionality of
pyroots is already implemented in
scipy, so the
natural question is why rediscover the wheel?
Well, the main reason is that
scipy is a huge dependency.
the other hand is just a single package that is easily installed and
that you can easily bundle with
py2exe or similar projects. It doesn't
even need to get installed, just throw the
pyroots folder in your
project and you are ready to go.
Apart from that, the API used by
scipy's functions is not very
user-friendly. For example you can't use keyword arguments for your
functions. Moreover, in
scipy there is no reliable way to define how
many digits of accuracy you want in the obtained root. For example, you
may ask for 6 digits, but scipy may calculate up to 14 (or 12 or
whatever) digits. The main implication of this "glitch" is that scipy's
method may evaluate the function more times than those really needed. If
the function calculates something trivial like the functions in the
following examples, then these extra function calls are no big deal, but
if your functions take significant time to evaluate ,e.g. more than
seconds, then this can quickly become annoying, or even, simply
unacceptable, e.g. the function takes some minutes to return a value.
pip install pyroots
All the solvers share the same API, so you can easily switch between the various methods.
The function whose root you are searching must take at least a single argument and return a single number. This first argument is also the dependent variable and, apart from that, the function can also take any number of positional/keyword arguments. For example the following functions are totally valid ones:
def f(x, a): return x ** 2 - a + 1 def g(x, a, b, c=3): return x ** 2 + a ** b - c
The first thing you have to do is to create a
Solver object for the
method you want to use:
from pyroots import Brentq brent = Brentq()
When you create the
Solver object, you can specify several parameters
that will affect the convergence. The most important are:
- epsilon which specifies the number of digits that will be taken
under consideration when checking for convergence. It defaults to
raise_on_failwhich will raise an exception if convergence failed. It defaults to True.
Using the above function definitions, in order to find the root of
you must first define an interval that contains the root. Let's say that
this interval is defined as
[xa, xb]. In this case you will call the
solver like this:
def f(x, a): return x ** 2 - a + 1 solver = Brentq() result = solver(f, xa, xb, a=3)
All the methods return a
Result object that has the following
result.x0 # the root result.fx0 # the value of ``f(x0)` result.convergence # True/False result.iterations # the number of iterations result.func_calls # the number of function evaluations. result.msg # a descriptive message regarding the convergence (or the failure of convergence) result.x_steps # a list containing the x values that have been tried while the solver run result.fx_steps # a list containing the f(x) values that have been calculated while the solver run
If, for some reason, convergence cannot be achieved, then a
ConvergenceError is raised. If you don't want that to happen, then you
have to pass
False as the value of
def f(x): return x ** 2 - 1 result = brent(f, xa=-10, xb=-5, raise_on_fail=False) print(result)
Each solver factory has the following signature:
SolverFactory(epsilon=1e-6, xtol=EPS, max_iter=500, raise_on_fail=True, debug_precision=10)
epsilonis the required precision of the solution, i.e. a solution is achieved when
|f(x0)|is smaller than
max_iteris the maximum allowed number of iterations.
raise_on_failis a boolean flag indicating whether or not an exception should be raised if convergence fails. It defaults to True
Each solver object has the following signature:
solver_object(f, xa, xb, *args, **kwargs)
fis the function whose root we are searching.
xais the lower bracket of the interval of the solution we search.
xbis the upper bracket of the interval of the solution we search.
*argsare passed as positional arguments when
**kwargsare passed as keyword arguments when
For the time being documentation is not yet ready, but the examples in the README should be enough to get your feet wet.
The source code repository of pyroots can be found at: https://github.com/pmav99/pyroots
Feedback and contributions are greatly appreciated.
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