scipy.optimize.fmin_l_bfgs_b¶

scipy.optimize.fmin_l_bfgs_b(func, x0, fprime=None, args=(), approx_grad=0, bounds=None, m=10, factr=10000000.0, pgtol=1e-05, epsilon=1e-08, iprint=-1, maxfun=15000, maxiter=15000, disp=None, callback=None, maxls=20)[source]¶

Minimize a function func using the L-BFGS-B algorithm.

Parameters:

Parameters:	func : callable f(x,args) Function to minimise. x0 : ndarray Initial guess. fprime* : callable fprime(x,args), optional The gradient of func. If None, then func* returns the function value and the gradient (`f, g = func(x, args)`), unless approx_grad* is True in which case func returns only `f`. args : sequence, optional Arguments to pass to func and fprime. approx_grad : bool, optional Whether to approximate the gradient numerically (in which case func returns only the function value). bounds : list, optional `(min, max)` pairs for each element in `x`, defining the bounds on that parameter. Use None or +-inf for one of `min` or `max` when there is no bound in that direction. m : int, optional The maximum number of variable metric corrections used to define the limited memory matrix. (The limited memory BFGS method does not store the full hessian but uses this many terms in an approximation to it.) factr : float, optional The iteration stops when `(f^k - f^{k+1})/max{\|f^k\|,\|f^{k+1}\|,1} <= factr * eps`, where `eps` is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy. pgtol : float, optional The iteration will stop when `max{\|proj g_i \| i = 1, ..., n} <= pgtol` where `pg_i` is the i-th component of the projected gradient. epsilon : float, optional Step size used when approx_grad is True, for numerically calculating the gradient iprint : int, optional Controls the frequency of output. `iprint < 0` means no output; `iprint = 0` print only one line at the last iteration; `0 < iprint < 99` print also f and `\|proj g\|` every iprint iterations; `iprint = 99` print details of every iteration except n-vectors; `iprint = 100` print also the changes of active set and final x; `iprint > 100` print details of every iteration including x and g. disp : int, optional If zero, then no output. If a positive number, then this over-rides iprint (i.e., iprint gets the value of disp). maxfun : int, optional Maximum number of function evaluations. maxiter : int, optional Maximum number of iterations. callback : callable, optional Called after each iteration, as `callback(xk)`, where `xk` is the current parameter vector. maxls : int, optional Maximum number of line search steps (per iteration). Default is 20.
Returns:	x : array_like Estimated position of the minimum. f : float Value of func at the minimum. d : dict Information dictionary. d[‘warnflag’] is 0 if converged, 1 if too many function evaluations or too many iterations, 2 if stopped for another reason, given in d[‘task’] d[‘grad’] is the gradient at the minimum (should be 0 ish) d[‘funcalls’] is the number of function calls made. d[‘nit’] is the number of iterations.

func : callable f(x,*args)

Function to minimise.

x0 : ndarray

Initial guess.

fprime : callable fprime(x,*args), optional

The gradient of func. If None, then func returns the function value and the gradient (f, g = func(x, *args)), unless approx_grad is True in which case func returns only f.

args : sequence, optional

Arguments to pass to func and fprime.

approx_grad : bool, optional

Whether to approximate the gradient numerically (in which case func returns only the function value).

bounds : list, optional

(min, max) pairs for each element in x, defining the bounds on that parameter. Use None or +-inf for one of min or max when there is no bound in that direction.

m : int, optional

The maximum number of variable metric corrections used to define the limited memory matrix. (The limited memory BFGS method does not store the full hessian but uses this many terms in an approximation to it.)

factr : float, optional

The iteration stops when (f^k - f^{k+1})/max{|f^k|,|f^{k+1}|,1} <= factr * eps, where eps is the machine precision, which is automatically generated by the code. Typical values for factr are: 1e12 for low accuracy; 1e7 for moderate accuracy; 10.0 for extremely high accuracy.

pgtol : float, optional

The iteration will stop when max{|proj g_i | i = 1, ..., n} <= pgtol where pg_i is the i-th component of the projected gradient.

epsilon : float, optional

Step size used when approx_grad is True, for numerically calculating the gradient

iprint : int, optional

Controls the frequency of output. iprint < 0 means no output; iprint = 0 print only one line at the last iteration; 0 < iprint < 99 print also f and |proj g| every iprint iterations; iprint = 99 print details of every iteration except n-vectors; iprint = 100 print also the changes of active set and final x; iprint > 100 print details of every iteration including x and g.

disp : int, optional

If zero, then no output. If a positive number, then this over-rides iprint (i.e., iprint gets the value of disp).

maxfun : int, optional

Maximum number of function evaluations.

maxiter : int, optional

Maximum number of iterations.

callback : callable, optional

Called after each iteration, as callback(xk), where xk is the current parameter vector.

maxls : int, optional

Maximum number of line search steps (per iteration). Default is 20.

Returns:

x : array_like

Estimated position of the minimum.

f : float

Value of func at the minimum.

d : dict

Information dictionary.

d[‘warnflag’] is

0 if converged,

1 if too many function evaluations or too many iterations,

2 if stopped for another reason, given in d[‘task’]

d[‘grad’] is the gradient at the minimum (should be 0 ish)

d[‘funcalls’] is the number of function calls made.

d[‘nit’] is the number of iterations.