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:
funccallable f(x,*args)

Function to minimize.

x0ndarray

Initial guess.

fprimecallable 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`.

argssequence, optional

Arguments to pass to func and fprime.

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

boundslist, 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.

mint, 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.)

factrfloat, 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. See Notes for relationship to ftol, which is exposed (instead of factr) by the `scipy.optimize.minimize` interface to L-BFGS-B.

pgtolfloat, optional

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

epsilonfloat, optional

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

iprintint, 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.

dispint, optional

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

maxfunint, optional

Maximum number of function evaluations. Note that this function may violate the limit because of evaluating gradients by numerical differentiation.

maxiterint, optional

Maximum number of iterations.

callbackcallable, optional

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

maxlsint, optional

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

Returns:
xarray_like

Estimated position of the minimum.

ffloat

Value of func at the minimum.

ddict

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.

`minimize`

Interface to minimization algorithms for multivariate functions. See the ‘L-BFGS-B’ method in particular. Note that the ftol option is made available via that interface, while factr is provided via this interface, where factr is the factor multiplying the default machine floating-point precision to arrive at ftol: `ftol = factr * numpy.finfo(float).eps`.

Notes

The version included here (in fortran code) is 3.0 (released April 25, 2011). It was written by Ciyou Zhu, Richard Byrd, and Jorge Nocedal <nocedal@ece.nwu.edu>. It carries the following condition for use:

This software is freely available, but we expect that all publications describing work using this software, or all commercial products using it, quote at least one of the references given below. This software is released under the BSD License.

References

• R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing, 16, 5, pp. 1190-1208.

• C. Zhu, R. H. Byrd and J. Nocedal. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (1997), ACM Transactions on Mathematical Software, 23, 4, pp. 550 - 560.

• J.L. Morales and J. Nocedal. L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (2011), ACM Transactions on Mathematical Software, 38, 1.

Examples

Solve a linear regression problem via `fmin_l_bfgs_b`. To do this, first we define an objective function `f(m, b) = (y - y_model)**2`, where y describes the observations and y_model the prediction of the linear model as `y_model = m*x + b`. The bounds for the parameters, `m` and `b`, are arbitrarily chosen as `(0,5)` and `(5,10)` for this example.

```>>> import numpy as np
>>> from scipy.optimize import fmin_l_bfgs_b
>>> X = np.arange(0, 10, 1)
>>> M = 2
>>> B = 3
>>> Y = M * X + B
>>> def func(parameters, *args):
...     x = args[0]
...     y = args[1]
...     m, b = parameters
...     y_model = m*x + b
...     error = sum(np.power((y - y_model), 2))
...     return error
```
```>>> initial_values = np.array([0.0, 1.0])
```
```>>> x_opt, f_opt, info = fmin_l_bfgs_b(func, x0=initial_values, args=(X, Y),
The optimized parameters in `x_opt` agree with the ground truth parameters `m` and `b`. Next, let us perform a bound constrained optimization using the bounds parameter.
```>>> bounds = [(0, 5), (5, 10)]