- bisect(f, a, b, args = (), xtol = _xtol, rtol = _rtol, maxiter = _iter, full_output = False, disp = False)
Find root of f in [a,b]
Basic bisection routine to find a zero of the function
f between the arguments a and b. f(a) and f(b) can not
have the same signs. Slow but sure.
f : Python function returning a number.
a : Number, one end of the bracketing interval.
b : Number, the other end of the bracketing interval.
xtol : Number, the routine converges when a root is known
to lie within xtol of the value return. Should be >= 0.
The routine modifies this to take into account the relative
precision of doubles.
maxiter : Number, if convergence is not achieved in
maxiter iterations, and error is raised. Must be
>= 0.
args : tuple containing extra arguments for the function f.
f is called by apply(f,(x)+args).
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 containing information
about the convergence. In particular, r.converged is True if the
the routine converged.
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
anneal, brute -- global optimizers
fminbound, brent, golden, bracket -- local scalar minimizers
fsolve -- n-dimenstional root-finding
brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
fixed_point -- scalar fixed-point finder
- brenth(f, a, b, args = (), xtol = _xtol, rtol = _rtol, maxiter = _iter, full_output = False, disp = False)
Find root of f in [a,b]
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) can not 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.
f : Python function returning a number.
a : Number, one end of the bracketing interval.
b : Number, the other end of the bracketing interval.
xtol : Number, the routine converges when a root is known
to lie within xtol of the value return. Should be >= 0.
The routine modifies this to take into account the relative
precision of doubles.
maxiter : Number, if convergence is not achieved in
maxiter iterations, and error is raised. Must be
>= 0.
args : tuple containing extra arguments for the function f.
f is called by apply(f,(x)+args).
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 containing information
about the convergence. In particular, r.converged is True if the
the routine converged.
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
anneal, brute -- global optimizers
fminbound, brent, golden, bracket -- local scalar minimizers
fsolve -- n-dimenstional root-finding
brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
fixed_point -- scalar fixed-point finder
- brentq(f, a, b, args = (), xtol = _xtol, rtol = _rtol, maxiter = _iter, full_output = False, disp = False)
Find root of f in [a,b]
The classic Brent routine to find a zero of the function
f between the arguments a and b. f(a) and f(b) can not
have the same signs. Generally the best of the routines here.
It is a safe version of the secant method that uses inverse
quadratic extrapolation. The version here is a slight
modification that uses a different formula in the extrapolation
step. A description may be found in Numerical Recipes, but the
code here is probably easier to understand.
f : Python function returning a number.
a : Number, one end of the bracketing interval.
b : Number, the other end of the bracketing interval.
xtol : Number, the routine converges when a root is known
to lie within xtol of the value return. Should be >= 0.
The routine modifies this to take into account the relative
precision of doubles.
maxiter : Number, if convergence is not achieved in
maxiter iterations, and error is raised. Must be
>= 0.
args : tuple containing extra arguments for the function f.
f is called by apply(f,(x)+args).
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 containing information
about the convergence. In particular, r.converged is True if the
the routine converged.
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
anneal, brute -- global optimizers
fminbound, brent, golden, bracket -- local scalar minimizers
fsolve -- n-dimenstional root-finding
brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
fixed_point -- scalar fixed-point finder
- results_c(full_output, r)
- ridder(f, a, b, args = (), xtol = _xtol, rtol = _rtol, maxiter = _iter, full_output = False, disp = False)
Find root of f in [a,b]
Ridder routine to find a zero of the function
f between the arguments a and b. f(a) and f(b) can not
have the same signs. Faster than bisection, but not
generaly as fast as the brent rountines. A description
may be found in a recent edition of Numerical Recipes.
The routine here is modified a bit to be more careful
of tolerance.
f : Python function returning a number.
a : Number, one end of the bracketing interval.
b : Number, the other end of the bracketing interval.
xtol : Number, the routine converges when a root is known
to lie within xtol of the value return. Should be >= 0.
The routine modifies this to take into account the relative
precision of doubles.
maxiter : Number, if convergence is not achieved in
maxiter iterations, and error is raised. Must be
>= 0.
args : tuple containing extra arguments for the function f.
f is called by apply(f,(x)+args).
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 containing information
about the convergence. In particular, r.converged is True if the
the routine converged.
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
anneal, brute -- global optimizers
fminbound, brent, golden, bracket -- local scalar minimizers
fsolve -- n-dimenstional root-finding
brentq, brenth, ridder, bisect, newton -- one-dimensional root-finding
fixed_point -- scalar fixed-point finder
SciPy 0.6.0 API Documentation Generated by Endo, 2007-10-17