from __future__ import division, print_function, absolute_import import warnings from collections import namedtuple import operator from . import _zeros import numpy as np _iter = 100 _xtol = 2e-12 _rtol = 4 * np.finfo(float).eps __all__ = ['newton', 'bisect', 'ridder', 'brentq', 'brenth', 'toms748', 'RootResults'] # Must agree with CONVERGED, SIGNERR, CONVERR, ... in zeros.h _ECONVERGED = 0 _ESIGNERR = -1 _ECONVERR = -2 _EVALUEERR = -3 _EINPROGRESS = 1 CONVERGED = 'converged' SIGNERR = 'sign error' CONVERR = 'convergence error' VALUEERR = 'value error' INPROGRESS = 'No error' flag_map = {_ECONVERGED: CONVERGED, _ESIGNERR: SIGNERR, _ECONVERR: CONVERR, _EVALUEERR: VALUEERR, _EINPROGRESS: INPROGRESS} class RootResults(object): """Represents the root finding result. Attributes ---------- root : float Estimated root location. iterations : int Number of iterations needed to find the root. function_calls : int Number of times the function was called. converged : bool True if the routine converged. flag : str Description of the cause of termination. """ def __init__(self, root, iterations, function_calls, flag): self.root = root self.iterations = iterations self.function_calls = function_calls self.converged = flag == _ECONVERGED self.flag = None try: self.flag = flag_map[flag] except KeyError: self.flag = 'unknown error %d' % (flag,) def __repr__(self): attrs = ['converged', 'flag', 'function_calls', 'iterations', 'root'] m = max(map(len, attrs)) + 1 return '\n'.join([a.rjust(m) + ': ' + repr(getattr(self, a)) for a in attrs]) def results_c(full_output, r): if full_output: x, funcalls, iterations, flag = r results = RootResults(root=x, iterations=iterations, function_calls=funcalls, flag=flag) return x, results else: return r def _results_select(full_output, r): """Select from a tuple of (root, funccalls, iterations, flag)""" x, funcalls, iterations, flag = r if full_output: results = RootResults(root=x, iterations=iterations, function_calls=funcalls, flag=flag) return x, results return x def newton(func, x0, fprime=None, args=(), tol=1.48e-8, maxiter=50, fprime2=None, x1=None, rtol=0.0, full_output=False, disp=True): """ Find a zero of a real or complex function using the Newton-Raphson (or secant or Halley's) method. Find a zero of the function `func` given a nearby starting point `x0`. The Newton-Raphson method is used if the derivative `fprime` of `func` is provided, otherwise the secant method is used. If the second order derivative `fprime2` of `func` is also provided, then Halley's method is used. If `x0` is a sequence with more than one item, then `newton` returns an array, and `func` must be vectorized and return a sequence or array of the same shape as its first argument. If `fprime` or `fprime2` is given then its return must also have the same shape. Parameters ---------- func : callable The function whose zero is wanted. It must be a function of a single variable of the form ``f(x,a,b,c...)``, where ``a,b,c...`` are extra arguments that can be passed in the `args` parameter. x0 : float, sequence, or ndarray An initial estimate of the zero that should be somewhere near the actual zero. If not scalar, then `func` must be vectorized and return a sequence or array of the same shape as its first argument. fprime : callable, optional The derivative of the function when available and convenient. If it is None (default), then the secant method is used. args : tuple, optional Extra arguments to be used in the function call. tol : float, optional The allowable error of the zero value. If `func` is complex-valued, a larger `tol` is recommended as both the real and imaginary parts of `x` contribute to ``|x - x0|``. maxiter : int, optional Maximum number of iterations. fprime2 : callable, optional The second order derivative of the function when available and convenient. If it is None (default), then the normal Newton-Raphson or the secant method is used. If it is not None, then Halley's method is used. x1 : float, optional Another estimate of the zero that should be somewhere near the actual zero. Used if `fprime` is not provided. rtol : float, optional Tolerance (relative) for termination. full_output : bool, optional If `full_output` is False (default), the root is returned. If True and `x0` is scalar, the return value is ``(x, r)``, where ``x`` is the root and ``r`` is a `RootResults` object. If True and `x0` is non-scalar, the return value is ``(x, converged, zero_der)`` (see Returns section for details). disp : bool, optional If True, raise a RuntimeError if the algorithm didn't converge, with the error message containing the number of iterations and current function value. Otherwise the convergence status is recorded in a `RootResults` return object. Ignored if `x0` is not scalar. *Note: this has little to do with displaying, however the `disp` keyword cannot be renamed for backwards compatibility.* Returns ------- root : float, sequence, or ndarray Estimated location where function is zero. r : `RootResults`, optional Present if ``full_output=True`` and `x0` is scalar. Object containing information about the convergence. In particular, ``r.converged`` is True if the routine converged. converged : ndarray of bool, optional Present if ``full_output=True`` and `x0` is non-scalar. For vector functions, indicates which elements converged successfully. zero_der : ndarray of bool, optional Present if ``full_output=True`` and `x0` is non-scalar. For vector functions, indicates which elements had a zero derivative. See Also -------- brentq, brenth, ridder, bisect fsolve : find zeros in n dimensions. Notes ----- The convergence rate of the Newton-Raphson method is quadratic, the Halley method is cubic, and the secant method is sub-quadratic. This means that if the function is well behaved the actual error in the estimated zero after the n-th iteration is approximately the square (cube for Halley) of the error after the (n-1)-th step. However, the stopping criterion used here is the step size and there is no guarantee that a zero has been found. Consequently the result should be verified. Safer algorithms are brentq, brenth, ridder, and bisect, but they all require that the root first be bracketed in an interval where the function changes sign. The brentq algorithm is recommended for general use in one dimensional problems when such an interval has been found. When `newton` is used with arrays, it is best suited for the following types of problems: * The initial guesses, `x0`, are all relatively the same distance from the roots. * Some or all of the extra arguments, `args`, are also arrays so that a class of similar problems can be solved together. * The size of the initial guesses, `x0`, is larger than O(100) elements. Otherwise, a naive loop may perform as well or better than a vector. Examples -------- >>> from scipy import optimize >>> import matplotlib.pyplot as plt >>> def f(x): ... return (x**3 - 1) # only one real root at x = 1 ``fprime`` is not provided, use the secant method: >>> root = optimize.newton(f, 1.5) >>> root 1.0000000000000016 >>> root = optimize.newton(f, 1.5, fprime2=lambda x: 6 * x) >>> root 1.0000000000000016 Only ``fprime`` is provided, use the Newton-Raphson method: >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2) >>> root 1.0 Both ``fprime2`` and ``fprime`` are provided, use Halley's method: >>> root = optimize.newton(f, 1.5, fprime=lambda x: 3 * x**2, ... fprime2=lambda x: 6 * x) >>> root 1.0 When we want to find zeros for a set of related starting values and/or function parameters, we can provide both of those as an array of inputs: >>> f = lambda x, a: x**3 - a >>> fder = lambda x, a: 3 * x**2 >>> np.random.seed(4321) >>> x = np.random.randn(100) >>> a = np.arange(-50, 50) >>> vec_res = optimize.newton(f, x, fprime=fder, args=(a, )) The above is the equivalent of solving for each value in ``(x, a)`` separately in a for-loop, just faster: >>> loop_res = [optimize.newton(f, x0, fprime=fder, args=(a0,)) ... for x0, a0 in zip(x, a)] >>> np.allclose(vec_res, loop_res) True Plot the results found for all values of ``a``: >>> analytical_result = np.sign(a) * np.abs(a)**(1/3) >>> fig = plt.figure() >>> ax = fig.add_subplot(111) >>> ax.plot(a, analytical_result, 'o') >>> ax.plot(a, vec_res, '.') >>> ax.set_xlabel('$a$') >>> ax.set_ylabel('$x$ where $f(x, a)=0$') >>> plt.show() """ if tol <= 0: raise ValueError("tol too small (%g <= 0)" % tol) maxiter = operator.index(maxiter) if maxiter < 1: raise ValueError("maxiter must be greater than 0") if np.size(x0) > 1: return _array_newton(func, x0, fprime, args, tol, maxiter, fprime2, full_output) # Convert to float (don't use float(x0); this works also for complex x0) p0 = 1.0 * x0 funcalls = 0 if fprime is not None: # Newton-Raphson method for itr in range(maxiter): # first evaluate fval fval = func(p0, *args) funcalls += 1 # If fval is 0, a root has been found, then terminate if fval == 0: return _results_select( full_output, (p0, funcalls, itr, _ECONVERGED)) fder = fprime(p0, *args) funcalls += 1 if fder == 0: msg = "Derivative was zero." if disp: msg += ( " Failed to converge after %d iterations, value is %s." % (itr + 1, p0)) raise RuntimeError(msg) warnings.warn(msg, RuntimeWarning) return _results_select( full_output, (p0, funcalls, itr + 1, _ECONVERR)) newton_step = fval / fder if fprime2: fder2 = fprime2(p0, *args) funcalls += 1 # Halley's method: # newton_step /= (1.0 - 0.5 * newton_step * fder2 / fder) # Only do it if denominator stays close enough to 1 # Rationale: If 1-adj < 0, then Halley sends x in the # opposite direction to Newton. Doesn't happen if x is close # enough to root. adj = newton_step * fder2 / fder / 2 if np.abs(adj) < 1: newton_step /= 1.0 - adj p = p0 - newton_step if np.isclose(p, p0, rtol=rtol, atol=tol): return _results_select( full_output, (p, funcalls, itr + 1, _ECONVERGED)) p0 = p else: # Secant method if x1 is not None: if x1 == x0: raise ValueError("x1 and x0 must be different") p1 = x1 else: eps = 1e-4 p1 = x0 * (1 + eps) p1 += (eps if p1 >= 0 else -eps) q0 = func(p0, *args) funcalls += 1 q1 = func(p1, *args) funcalls += 1 if abs(q1) < abs(q0): p0, p1, q0, q1 = p1, p0, q1, q0 for itr in range(maxiter): if q1 == q0: if p1 != p0: msg = "Tolerance of %s reached." % (p1 - p0) if disp: msg += ( " Failed to converge after %d iterations, value is %s." % (itr + 1, p1)) raise RuntimeError(msg) warnings.warn(msg, RuntimeWarning) p = (p1 + p0) / 2.0 return _results_select( full_output, (p, funcalls, itr + 1, _ECONVERGED)) else: if abs(q1) > abs(q0): p = (-q0 / q1 * p1 + p0) / (1 - q0 / q1) else: p = (-q1 / q0 * p0 + p1) / (1 - q1 / q0) if np.isclose(p, p1, rtol=rtol, atol=tol): return _results_select( full_output, (p, funcalls, itr + 1, _ECONVERGED)) p0, q0 = p1, q1 p1 = p q1 = func(p1, *args) funcalls += 1 if disp: msg = ("Failed to converge after %d iterations, value is %s." % (itr + 1, p)) raise RuntimeError(msg) return _results_select(full_output, (p, funcalls, itr + 1, _ECONVERR)) def _array_newton(func, x0, fprime, args, tol, maxiter, fprime2, full_output): """ A vectorized version of Newton, Halley, and secant methods for arrays. Do not use this method directly. This method is called from `newton` when ``np.size(x0) > 1`` is ``True``. For docstring, see `newton`. """ # Explicitly copy `x0` as `p` will be modified inplace, but, the # user's array should not be altered. try: p = np.array(x0, copy=True, dtype=float) except TypeError: # can't convert complex to float p = np.array(x0, copy=True) failures = np.ones_like(p, dtype=bool) nz_der = np.ones_like(failures) if fprime is not None: # Newton-Raphson method for iteration in range(maxiter): # first evaluate fval fval = np.asarray(func(p, *args)) # If all fval are 0, all roots have been found, then terminate if not fval.any(): failures = fval.astype(bool) break fder = np.asarray(fprime(p, *args)) nz_der = (fder != 0) # stop iterating if all derivatives are zero if not nz_der.any(): break # Newton step dp = fval[nz_der] / fder[nz_der] if fprime2 is not None: fder2 = np.asarray(fprime2(p, *args)) dp = dp / (1.0 - 0.5 * dp * fder2[nz_der] / fder[nz_der]) # only update nonzero derivatives p[nz_der] -= dp failures[nz_der] = np.abs(dp) >= tol # items not yet converged # stop iterating if there aren't any failures, not incl zero der if not failures[nz_der].any(): break else: # Secant method dx = np.finfo(float).eps**0.33 p1 = p * (1 + dx) + np.where(p >= 0, dx, -dx) q0 = np.asarray(func(p, *args)) q1 = np.asarray(func(p1, *args)) active = np.ones_like(p, dtype=bool) for iteration in range(maxiter): nz_der = (q1 != q0) # stop iterating if all derivatives are zero if not nz_der.any(): p = (p1 + p) / 2.0 break # Secant Step dp = (q1 * (p1 - p))[nz_der] / (q1 - q0)[nz_der] # only update nonzero derivatives p[nz_der] = p1[nz_der] - dp active_zero_der = ~nz_der & active p[active_zero_der] = (p1 + p)[active_zero_der] / 2.0 active &= nz_der # don't assign zero derivatives again failures[nz_der] = np.abs(dp) >= tol # not yet converged # stop iterating if there aren't any failures, not incl zero der if not failures[nz_der].any(): break p1, p = p, p1 q0 = q1 q1 = np.asarray(func(p1, *args)) zero_der = ~nz_der & failures # don't include converged with zero-ders if zero_der.any(): # Secant warnings if fprime is None: nonzero_dp = (p1 != p) # non-zero dp, but infinite newton step zero_der_nz_dp = (zero_der & nonzero_dp) if zero_der_nz_dp.any(): rms = np.sqrt( sum((p1[zero_der_nz_dp] - p[zero_der_nz_dp]) ** 2) ) warnings.warn( 'RMS of {:g} reached'.format(rms), RuntimeWarning) # Newton or Halley warnings else: all_or_some = 'all' if zero_der.all() else 'some' msg = '{:s} derivatives were zero'.format(all_or_some) warnings.warn(msg, RuntimeWarning) elif failures.any(): all_or_some = 'all' if failures.all() else 'some' msg = '{0:s} failed to converge after {1:d} iterations'.format( all_or_some, maxiter ) if failures.all(): raise RuntimeError(msg) warnings.warn(msg, RuntimeWarning) if full_output: result = namedtuple('result', ('root', 'converged', 'zero_der')) p = result(p, ~failures, zero_der) return p def bisect(f, a, b, args=(), xtol=_xtol, rtol=_rtol, maxiter=_iter, full_output=False, disp=True): """ Find root of a function within an interval using bisection. Basic bisection routine to find a zero of the function `f` between the arguments `a` and `b`. `f(a)` and `f(b)` cannot have the same signs. Slow but sure. Parameters ---------- f : function Python function returning a number. `f` must be continuous, and f(a) and f(b) must have opposite signs. a : scalar One end of the bracketing interval [a,b]. b : scalar The other end of the bracketing interval [a,b]. xtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter must be nonnegative. rtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter cannot be smaller than its default value of ``4*np.finfo(float).eps``. maxiter : int, optional if convergence is not achieved in `maxiter` iterations, an error is raised. Must be >= 0. args : tuple, optional containing extra arguments for the function `f`. `f` is called by ``apply(f, (x)+args)``. full_output : bool, optional If `full_output` is False, the root is returned. If `full_output` is True, the return value is ``(x, r)``, where x is the root, and r is a `RootResults` object. disp : bool, optional If True, raise RuntimeError if the algorithm didn't converge. Otherwise the convergence status is recorded in a `RootResults` return object. Returns ------- x0 : float Zero of `f` between `a` and `b`. r : `RootResults` (present if ``full_output = True``) Object containing information about the convergence. In particular, ``r.converged`` is True if the routine converged. Examples -------- >>> def f(x): ... return (x**2 - 1) >>> from scipy import optimize >>> root = optimize.bisect(f, 0, 2) >>> root 1.0 >>> root = optimize.bisect(f, -2, 0) >>> root -1.0 See Also -------- brentq, brenth, bisect, newton fixed_point : scalar fixed-point finder fsolve : n-dimensional root-finding """ if not isinstance(args, tuple): args = (args,) maxiter = operator.index(maxiter) if xtol <= 0: raise ValueError("xtol too small (%g <= 0)" % xtol) if rtol < _rtol: raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol)) r = _zeros._bisect(f, a, b, xtol, rtol, maxiter, args, full_output, disp) return results_c(full_output, r) def ridder(f, a, b, args=(), xtol=_xtol, rtol=_rtol, maxiter=_iter, full_output=False, disp=True): """ Find a root of a function in an interval using Ridder's method. Parameters ---------- f : function Python function returning a number. f must be continuous, and f(a) and f(b) must have opposite signs. a : scalar One end of the bracketing interval [a,b]. b : scalar The other end of the bracketing interval [a,b]. xtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter must be nonnegative. rtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter cannot be smaller than its default value of ``4*np.finfo(float).eps``. maxiter : int, optional if convergence is not achieved in `maxiter` iterations, an error is raised. Must be >= 0. args : tuple, optional containing extra arguments for the function `f`. `f` is called by ``apply(f, (x)+args)``. full_output : bool, optional If `full_output` is False, the root is returned. If `full_output` is True, the return value is ``(x, r)``, where `x` is the root, and `r` is a `RootResults` object. disp : bool, optional If True, raise RuntimeError if the algorithm didn't converge. Otherwise the convergence status is recorded in any `RootResults` return object. Returns ------- x0 : float Zero of `f` between `a` and `b`. r : `RootResults` (present if ``full_output = True``) Object containing information about the convergence. In particular, ``r.converged`` is True if the routine converged. See Also -------- brentq, brenth, bisect, newton : one-dimensional root-finding fixed_point : scalar fixed-point finder Notes ----- Uses [Ridders1979]_ method to find a zero of the function `f` between the arguments `a` and `b`. Ridders' method is faster than bisection, but not generally as fast as the Brent routines. [Ridders1979]_ provides the classic description and source of the algorithm. A description can also be found in any recent edition of Numerical Recipes. The routine used here diverges slightly from standard presentations in order to be a bit more careful of tolerance. References ---------- .. [Ridders1979] Ridders, C. F. J. "A New Algorithm for Computing a Single Root of a Real Continuous Function." IEEE Trans. Circuits Systems 26, 979-980, 1979. Examples -------- >>> def f(x): ... return (x**2 - 1) >>> from scipy import optimize >>> root = optimize.ridder(f, 0, 2) >>> root 1.0 >>> root = optimize.ridder(f, -2, 0) >>> root -1.0 """ if not isinstance(args, tuple): args = (args,) maxiter = operator.index(maxiter) if xtol <= 0: raise ValueError("xtol too small (%g <= 0)" % xtol) if rtol < _rtol: raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol)) r = _zeros._ridder(f, a, b, xtol, rtol, maxiter, args, full_output, disp) return results_c(full_output, r) def brentq(f, a, b, args=(), xtol=_xtol, rtol=_rtol, maxiter=_iter, full_output=False, disp=True): """ Find a root of a function in a bracketing interval using Brent's method. Uses the classic Brent's method to find a zero of the function `f` on the sign changing interval [a , b]. Generally considered the best of the rootfinding routines here. It is a safe version of the secant method that uses inverse quadratic extrapolation. Brent's method combines root bracketing, interval bisection, and inverse quadratic interpolation. It is sometimes known as the van Wijngaarden-Dekker-Brent method. Brent (1973) claims convergence is guaranteed for functions computable within [a,b]. [Brent1973]_ provides the classic description of the algorithm. Another description can be found in a recent edition of Numerical Recipes, including [PressEtal1992]_. A third description is at http://mathworld.wolfram.com/BrentsMethod.html. It should be easy to understand the algorithm just by reading our code. Our code diverges a bit from standard presentations: we choose a different formula for the extrapolation step. Parameters ---------- f : function Python function returning a number. The function :math:`f` must be continuous, and :math:`f(a)` and :math:`f(b)` must have opposite signs. a : scalar One end of the bracketing interval :math:`[a, b]`. b : scalar The other end of the bracketing interval :math:`[a, b]`. xtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter must be nonnegative. For nice functions, Brent's method will often satisfy the above condition with ``xtol/2`` and ``rtol/2``. [Brent1973]_ rtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter cannot be smaller than its default value of ``4*np.finfo(float).eps``. For nice functions, Brent's method will often satisfy the above condition with ``xtol/2`` and ``rtol/2``. [Brent1973]_ maxiter : int, optional if convergence is not achieved in `maxiter` iterations, an error is raised. Must be >= 0. args : tuple, optional containing extra arguments for the function `f`. `f` is called by ``apply(f, (x)+args)``. full_output : bool, optional If `full_output` is False, the root is returned. If `full_output` is True, the return value is ``(x, r)``, where `x` is the root, and `r` is a `RootResults` object. disp : bool, optional If True, raise RuntimeError if the algorithm didn't converge. Otherwise the convergence status is recorded in any `RootResults` return object. Returns ------- x0 : float Zero of `f` between `a` and `b`. r : `RootResults` (present if ``full_output = True``) Object containing information about the convergence. In particular, ``r.converged`` is True if the routine converged. Notes ----- `f` must be continuous. f(a) and f(b) must have opposite signs. Related functions fall into several classes: multivariate local optimizers `fmin`, `fmin_powell`, `fmin_cg`, `fmin_bfgs`, `fmin_ncg` nonlinear least squares minimizer `leastsq` constrained multivariate optimizers `fmin_l_bfgs_b`, `fmin_tnc`, `fmin_cobyla` global optimizers `basinhopping`, `brute`, `differential_evolution` local scalar minimizers `fminbound`, `brent`, `golden`, `bracket` n-dimensional root-finding `fsolve` one-dimensional root-finding `brenth`, `ridder`, `bisect`, `newton` scalar fixed-point finder `fixed_point` References ---------- .. [Brent1973] Brent, R. P., *Algorithms for Minimization Without Derivatives*. Englewood Cliffs, NJ: Prentice-Hall, 1973. Ch. 3-4. .. [PressEtal1992] Press, W. H.; Flannery, B. P.; Teukolsky, S. A.; and Vetterling, W. T. *Numerical Recipes in FORTRAN: The Art of Scientific Computing*, 2nd ed. Cambridge, England: Cambridge University Press, pp. 352-355, 1992. Section 9.3: "Van Wijngaarden-Dekker-Brent Method." Examples -------- >>> def f(x): ... return (x**2 - 1) >>> from scipy import optimize >>> root = optimize.brentq(f, -2, 0) >>> root -1.0 >>> root = optimize.brentq(f, 0, 2) >>> root 1.0 """ if not isinstance(args, tuple): args = (args,) maxiter = operator.index(maxiter) if xtol <= 0: raise ValueError("xtol too small (%g <= 0)" % xtol) if rtol < _rtol: raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol)) r = _zeros._brentq(f, a, b, xtol, rtol, maxiter, args, full_output, disp) return results_c(full_output, r) def brenth(f, a, b, args=(), xtol=_xtol, rtol=_rtol, maxiter=_iter, full_output=False, disp=True): """Find a root of a function in a bracketing interval using Brent's method with hyperbolic extrapolation. A variation on the classic Brent routine to find a zero of the function f between the arguments a and b that uses hyperbolic extrapolation instead of inverse quadratic extrapolation. There was a paper back in the 1980's ... f(a) and f(b) cannot have the same signs. Generally on a par with the brent routine, but not as heavily tested. It is a safe version of the secant method that uses hyperbolic extrapolation. The version here is by Chuck Harris. Parameters ---------- f : function Python function returning a number. f must be continuous, and f(a) and f(b) must have opposite signs. a : scalar One end of the bracketing interval [a,b]. b : scalar The other end of the bracketing interval [a,b]. xtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter must be nonnegative. As with `brentq`, for nice functions the method will often satisfy the above condition with ``xtol/2`` and ``rtol/2``. rtol : number, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter cannot be smaller than its default value of ``4*np.finfo(float).eps``. As with `brentq`, for nice functions the method will often satisfy the above condition with ``xtol/2`` and ``rtol/2``. maxiter : int, optional if convergence is not achieved in `maxiter` iterations, an error is raised. Must be >= 0. args : tuple, optional containing extra arguments for the function `f`. `f` is called by ``apply(f, (x)+args)``. full_output : bool, optional If `full_output` is False, the root is returned. If `full_output` is True, the return value is ``(x, r)``, where `x` is the root, and `r` is a `RootResults` object. disp : bool, optional If True, raise RuntimeError if the algorithm didn't converge. Otherwise the convergence status is recorded in any `RootResults` return object. Returns ------- x0 : float Zero of `f` between `a` and `b`. r : `RootResults` (present if ``full_output = True``) Object containing information about the convergence. In particular, ``r.converged`` is True if the routine converged. Examples -------- >>> def f(x): ... return (x**2 - 1) >>> from scipy import optimize >>> root = optimize.brenth(f, -2, 0) >>> root -1.0 >>> root = optimize.brenth(f, 0, 2) >>> root 1.0 See Also -------- fmin, fmin_powell, fmin_cg, fmin_bfgs, fmin_ncg : multivariate local optimizers leastsq : nonlinear least squares minimizer fmin_l_bfgs_b, fmin_tnc, fmin_cobyla : constrained multivariate optimizers basinhopping, differential_evolution, brute : global optimizers fminbound, brent, golden, bracket : local scalar minimizers fsolve : n-dimensional root-finding brentq, brenth, ridder, bisect, newton : one-dimensional root-finding fixed_point : scalar fixed-point finder """ if not isinstance(args, tuple): args = (args,) maxiter = operator.index(maxiter) if xtol <= 0: raise ValueError("xtol too small (%g <= 0)" % xtol) if rtol < _rtol: raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol)) r = _zeros._brenth(f, a, b, xtol, rtol, maxiter, args, full_output, disp) return results_c(full_output, r) ################################ # TOMS "Algorithm 748: Enclosing Zeros of Continuous Functions", by # Alefeld, G. E. and Potra, F. A. and Shi, Yixun, # See [1] def _within_tolerance(x, y, rtol, atol): diff = np.abs(x - y) z = np.abs(y) result = (diff <= (atol + rtol * z)) return result def _notclose(fs, rtol=_rtol, atol=_xtol): # Ensure not None, not 0, all finite, and not very close to each other notclosefvals = ( all(fs) and all(np.isfinite(fs)) and not any(any(np.isclose(_f, fs[i + 1:], rtol=rtol, atol=atol)) for i, _f in enumerate(fs[:-1]))) return notclosefvals def _secant(xvals, fvals): """Perform a secant step, taking a little care""" # Secant has many "mathematically" equivalent formulations # x2 = x0 - (x1 - x0)/(f1 - f0) * f0 # = x1 - (x1 - x0)/(f1 - f0) * f1 # = (-x1 * f0 + x0 * f1) / (f1 - f0) # = (-f0 / f1 * x1 + x0) / (1 - f0 / f1) # = (-f1 / f0 * x0 + x1) / (1 - f1 / f0) x0, x1 = xvals[:2] f0, f1 = fvals[:2] if f0 == f1: return np.nan if np.abs(f1) > np.abs(f0): x2 = (-f0 / f1 * x1 + x0) / (1 - f0 / f1) else: x2 = (-f1 / f0 * x0 + x1) / (1 - f1 / f0) return x2 def _update_bracket(ab, fab, c, fc): """Update a bracket given (c, fc), return the discarded endpoints.""" fa, fb = fab idx = (0 if np.sign(fa) * np.sign(fc) > 0 else 1) rx, rfx = ab[idx], fab[idx] fab[idx] = fc ab[idx] = c return rx, rfx def _compute_divided_differences(xvals, fvals, N=None, full=True, forward=True): """Return a matrix of divided differences for the xvals, fvals pairs DD[i, j] = f[x_{i-j}, ..., x_i] for 0 <= j <= i If full is False, just return the main diagonal(or last row): f[a], f[a, b] and f[a, b, c]. If forward is False, return f[c], f[b, c], f[a, b, c].""" if full: if forward: xvals = np.asarray(xvals) else: xvals = np.array(xvals)[::-1] M = len(xvals) N = M if N is None else min(N, M) DD = np.zeros([M, N]) DD[:, 0] = fvals[:] for i in range(1, N): DD[i:, i] = (np.diff(DD[i - 1:, i - 1]) / (xvals[i:] - xvals[:M - i])) return DD xvals = np.asarray(xvals) dd = np.array(fvals) row = np.array(fvals) idx2Use = (0 if forward else -1) dd[0] = fvals[idx2Use] for i in range(1, len(xvals)): denom = xvals[i:i + len(row) - 1] - xvals[:len(row) - 1] row = np.diff(row)[:] / denom dd[i] = row[idx2Use] return dd def _interpolated_poly(xvals, fvals, x): """Compute p(x) for the polynomial passing through the specified locations. Use Neville's algorithm to compute p(x) where p is the minimal degree polynomial passing through the points xvals, fvals""" xvals = np.asarray(xvals) N = len(xvals) Q = np.zeros([N, N]) D = np.zeros([N, N]) Q[:, 0] = fvals[:] D[:, 0] = fvals[:] for k in range(1, N): alpha = D[k:, k - 1] - Q[k - 1:N - 1, k - 1] diffik = xvals[0:N - k] - xvals[k:N] Q[k:, k] = (xvals[k:] - x) / diffik * alpha D[k:, k] = (xvals[:N - k] - x) / diffik * alpha # Expect Q[-1, 1:] to be small relative to Q[-1, 0] as x approaches a root return np.sum(Q[-1, 1:]) + Q[-1, 0] def _inverse_poly_zero(a, b, c, d, fa, fb, fc, fd): """Inverse cubic interpolation f-values -> x-values Given four points (fa, a), (fb, b), (fc, c), (fd, d) with fa, fb, fc, fd all distinct, find poly IP(y) through the 4 points and compute x=IP(0). """ return _interpolated_poly([fa, fb, fc, fd], [a, b, c, d], 0) def _newton_quadratic(ab, fab, d, fd, k): """Apply Newton-Raphson like steps, using divided differences to approximate f' ab is a real interval [a, b] containing a root, fab holds the real values of f(a), f(b) d is a real number outside [ab, b] k is the number of steps to apply """ a, b = ab fa, fb = fab _, B, A = _compute_divided_differences([a, b, d], [fa, fb, fd], forward=True, full=False) # _P is the quadratic polynomial through the 3 points def _P(x): # Horner evaluation of fa + B * (x - a) + A * (x - a) * (x - b) return (A * (x - b) + B) * (x - a) + fa if A == 0: r = a - fa / B else: r = (a if np.sign(A) * np.sign(fa) > 0 else b) # Apply k Newton-Raphson steps to _P(x), starting from x=r for i in range(k): r1 = r - _P(r) / (B + A * (2 * r - a - b)) if not (ab[0] < r1 < ab[1]): if (ab[0] < r < ab[1]): return r r = sum(ab) / 2.0 break r = r1 return r class TOMS748Solver(object): """Solve f(x, *args) == 0 using Algorithm748 of Alefeld, Potro & Shi. """ _MU = 0.5 _K_MIN = 1 _K_MAX = 100 # A very high value for real usage. Expect 1, 2, maybe 3. def __init__(self): self.f = None self.args = None self.function_calls = 0 self.iterations = 0 self.k = 2 # ab=[a,b] is a global interval containing a root self.ab = [np.nan, np.nan] # fab is function values at a, b self.fab = [np.nan, np.nan] self.d = None self.fd = None self.e = None self.fe = None self.disp = False self.xtol = _xtol self.rtol = _rtol self.maxiter = _iter def configure(self, xtol, rtol, maxiter, disp, k): self.disp = disp self.xtol = xtol self.rtol = rtol self.maxiter = maxiter # Silently replace a low value of k with 1 self.k = max(k, self._K_MIN) # Noisily replace a high value of k with self._K_MAX if self.k > self._K_MAX: msg = "toms748: Overriding k: ->%d" % self._K_MAX warnings.warn(msg, RuntimeWarning) self.k = self._K_MAX def _callf(self, x, error=True): """Call the user-supplied function, update book-keeping""" fx = self.f(x, *self.args) self.function_calls += 1 if not np.isfinite(fx) and error: raise ValueError("Invalid function value: f(%f) -> %s " % (x, fx)) return fx def get_result(self, x, flag=_ECONVERGED): r"""Package the result and statistics into a tuple.""" return (x, self.function_calls, self.iterations, flag) def _update_bracket(self, c, fc): return _update_bracket(self.ab, self.fab, c, fc) def start(self, f, a, b, args=()): r"""Prepare for the iterations.""" self.function_calls = 0 self.iterations = 0 self.f = f self.args = args self.ab[:] = [a, b] if not np.isfinite(a) or np.imag(a) != 0: raise ValueError("Invalid x value: %s " % (a)) if not np.isfinite(b) or np.imag(b) != 0: raise ValueError("Invalid x value: %s " % (b)) fa = self._callf(a) if not np.isfinite(fa) or np.imag(fa) != 0: raise ValueError("Invalid function value: f(%f) -> %s " % (a, fa)) if fa == 0: return _ECONVERGED, a fb = self._callf(b) if not np.isfinite(fb) or np.imag(fb) != 0: raise ValueError("Invalid function value: f(%f) -> %s " % (b, fb)) if fb == 0: return _ECONVERGED, b if np.sign(fb) * np.sign(fa) > 0: raise ValueError("a, b must bracket a root f(%e)=%e, f(%e)=%e " % (a, fa, b, fb)) self.fab[:] = [fa, fb] return _EINPROGRESS, sum(self.ab) / 2.0 def get_status(self): """Determine the current status.""" a, b = self.ab[:2] if _within_tolerance(a, b, self.rtol, self.xtol): return _ECONVERGED, sum(self.ab) / 2.0 if self.iterations >= self.maxiter: return _ECONVERR, sum(self.ab) / 2.0 return _EINPROGRESS, sum(self.ab) / 2.0 def iterate(self): """Perform one step in the algorithm. Implements Algorithm 4.1(k=1) or 4.2(k=2) in [APS1995] """ self.iterations += 1 eps = np.finfo(float).eps d, fd, e, fe = self.d, self.fd, self.e, self.fe ab_width = self.ab[1] - self.ab[0] # Need the start width below c = None for nsteps in range(2, self.k+2): # If the f-values are sufficiently separated, perform an inverse # polynomial interpolation step. Otherwise nsteps repeats of # an approximate Newton-Raphson step. if _notclose(self.fab + [fd, fe], rtol=0, atol=32*eps): c0 = _inverse_poly_zero(self.ab[0], self.ab[1], d, e, self.fab[0], self.fab[1], fd, fe) if self.ab[0] < c0 < self.ab[1]: c = c0 if c is None: c = _newton_quadratic(self.ab, self.fab, d, fd, nsteps) fc = self._callf(c) if fc == 0: return _ECONVERGED, c # re-bracket e, fe = d, fd d, fd = self._update_bracket(c, fc) # u is the endpoint with the smallest f-value uix = (0 if np.abs(self.fab[0]) < np.abs(self.fab[1]) else 1) u, fu = self.ab[uix], self.fab[uix] _, A = _compute_divided_differences(self.ab, self.fab, forward=(uix == 0), full=False) c = u - 2 * fu / A if np.abs(c - u) > 0.5 * (self.ab[1] - self.ab[0]): c = sum(self.ab) / 2.0 else: if np.isclose(c, u, rtol=eps, atol=0): # c didn't change (much). # Either because the f-values at the endpoints have vastly # differing magnitudes, or because the root is very close to # that endpoint frs = np.frexp(self.fab)[1] if frs[uix] < frs[1 - uix] - 50: # Differ by more than 2**50 c = (31 * self.ab[uix] + self.ab[1 - uix]) / 32 else: # Make a bigger adjustment, about the # size of the requested tolerance. mm = (1 if uix == 0 else -1) adj = mm * np.abs(c) * self.rtol + mm * self.xtol c = u + adj if not self.ab[0] < c < self.ab[1]: c = sum(self.ab) / 2.0 fc = self._callf(c) if fc == 0: return _ECONVERGED, c e, fe = d, fd d, fd = self._update_bracket(c, fc) # If the width of the new interval did not decrease enough, bisect if self.ab[1] - self.ab[0] > self._MU * ab_width: e, fe = d, fd z = sum(self.ab) / 2.0 fz = self._callf(z) if fz == 0: return _ECONVERGED, z d, fd = self._update_bracket(z, fz) # Record d and e for next iteration self.d, self.fd = d, fd self.e, self.fe = e, fe status, xn = self.get_status() return status, xn def solve(self, f, a, b, args=(), xtol=_xtol, rtol=_rtol, k=2, maxiter=_iter, disp=True): r"""Solve f(x) = 0 given an interval containing a zero.""" self.configure(xtol=xtol, rtol=rtol, maxiter=maxiter, disp=disp, k=k) status, xn = self.start(f, a, b, args) if status == _ECONVERGED: return self.get_result(xn) # The first step only has two x-values. c = _secant(self.ab, self.fab) if not self.ab[0] < c < self.ab[1]: c = sum(self.ab) / 2.0 fc = self._callf(c) if fc == 0: return self.get_result(c) self.d, self.fd = self._update_bracket(c, fc) self.e, self.fe = None, None self.iterations += 1 while True: status, xn = self.iterate() if status == _ECONVERGED: return self.get_result(xn) if status == _ECONVERR: fmt = "Failed to converge after %d iterations, bracket is %s" if disp: msg = fmt % (self.iterations + 1, self.ab) raise RuntimeError(msg) return self.get_result(xn, _ECONVERR) def toms748(f, a, b, args=(), k=1, xtol=_xtol, rtol=_rtol, maxiter=_iter, full_output=False, disp=True): """ Find a zero using TOMS Algorithm 748 method. Implements the Algorithm 748 method of Alefeld, Potro and Shi to find a zero of the function `f` on the interval `[a , b]`, where `f(a)` and `f(b)` must have opposite signs. It uses a mixture of inverse cubic interpolation and "Newton-quadratic" steps. [APS1995]. Parameters ---------- f : function Python function returning a scalar. The function :math:`f` must be continuous, and :math:`f(a)` and :math:`f(b)` have opposite signs. a : scalar, lower boundary of the search interval b : scalar, upper boundary of the search interval args : tuple, optional containing extra arguments for the function `f`. `f` is called by ``f(x, *args)``. k : int, optional The number of Newton quadratic steps to perform each iteration. ``k>=1``. xtol : scalar, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. The parameter must be nonnegative. rtol : scalar, optional The computed root ``x0`` will satisfy ``np.allclose(x, x0, atol=xtol, rtol=rtol)``, where ``x`` is the exact root. maxiter : int, optional if convergence is not achieved in `maxiter` iterations, an error is raised. Must be >= 0. full_output : bool, optional If `full_output` is False, the root is returned. If `full_output` is True, the return value is ``(x, r)``, where `x` is the root, and `r` is a `RootResults` object. disp : bool, optional If True, raise RuntimeError if the algorithm didn't converge. Otherwise the convergence status is recorded in the `RootResults` return object. Returns ------- x0 : float Approximate Zero of `f` r : `RootResults` (present if ``full_output = True``) Object containing information about the convergence. In particular, ``r.converged`` is True if the routine converged. See Also -------- brentq, brenth, ridder, bisect, newton fsolve : find zeroes in n dimensions. Notes ----- `f` must be continuous. Algorithm 748 with ``k=2`` is asymptotically the most efficient algorithm known for finding roots of a four times continuously differentiable function. In contrast with Brent's algorithm, which may only decrease the length of the enclosing bracket on the last step, Algorithm 748 decreases it each iteration with the same asymptotic efficiency as it finds the root. For easy statement of efficiency indices, assume that `f` has 4 continuouous deriviatives. For ``k=1``, the convergence order is at least 2.7, and with about asymptotically 2 function evaluations per iteration, the efficiency index is approximately 1.65. For ``k=2``, the order is about 4.6 with asymptotically 3 function evaluations per iteration, and the efficiency index 1.66. For higher values of `k`, the efficiency index approaches the `k`-th root of ``(3k-2)``, hence ``k=1`` or ``k=2`` are usually appropriate. References ---------- .. [APS1995] Alefeld, G. E. and Potra, F. A. and Shi, Yixun, *Algorithm 748: Enclosing Zeros of Continuous Functions*, ACM Trans. Math. Softw. Volume 221(1995) doi = {10.1145/210089.210111} Examples -------- >>> def f(x): ... return (x**3 - 1) # only one real root at x = 1 >>> from scipy import optimize >>> root, results = optimize.toms748(f, 0, 2, full_output=True) >>> root 1.0 >>> results converged: True flag: 'converged' function_calls: 11 iterations: 5 root: 1.0 """ if xtol <= 0: raise ValueError("xtol too small (%g <= 0)" % xtol) if rtol < _rtol / 4: raise ValueError("rtol too small (%g < %g)" % (rtol, _rtol)) maxiter = operator.index(maxiter) if maxiter < 1: raise ValueError("maxiter must be greater than 0") if not np.isfinite(a): raise ValueError("a is not finite %s" % a) if not np.isfinite(b): raise ValueError("b is not finite %s" % b) if a >= b: raise ValueError("a and b are not an interval [%d, %d]" % (a, b)) if not k >= 1: raise ValueError("k too small (%s < 1)" % k) if not isinstance(args, tuple): args = (args,) solver = TOMS748Solver() result = solver.solve(f, a, b, args=args, k=k, xtol=xtol, rtol=rtol, maxiter=maxiter, disp=disp) x, function_calls, iterations, flag = result return _results_select(full_output, (x, function_calls, iterations, flag))