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scipy.optimize.

# fmin_bfgs

scipy.optimize.fmin_bfgs(f, x0, fprime=None, args=(), gtol=1e-05, norm=inf, epsilon=np.float64(1.4901161193847656e-08), maxiter=None, full_output=0, disp=1, retall=0, callback=None, xrtol=0, c1=0.0001, c2=0.9, hess_inv0=None)

  Minimize a function using the BFGS algorithm.

  Parameters:
    f callable `f(x,*args)`

      Objective function to be minimized.

    x0 ndarray

      Initial guess, shape (n,)

    fprime callable `f'(x,*args)`, optional

      Gradient of f.

    args tuple, optional

      Extra arguments passed to f and fprime.

    gtol float, optional

      Terminate successfully if gradient norm is less than gtol

    norm float, optional

      Order of norm (Inf is max, -Inf is min)

    epsilon int or ndarray, optional

      If fprime is approximated, use this value for the step size.

    callback callable, optional

      An optional user-supplied function to call after each iteration. Called as `callback(xk)`, where `xk` is the current parameter vector.

    maxiter int, optional

      Maximum number of iterations to perform.

    full_output bool, optional

      If True, return `fopt`, `func_calls`, `grad_calls`, and `warnflag` in addition to `xopt`.

    disp bool, optional

      Print convergence message if True.

    retall bool, optional

      Return a list of results at each iteration if True.

    xrtol float, default: 0

      Relative tolerance for x. Terminate successfully if step size is less than `xk * xrtol` where `xk` is the current parameter vector.

    c1 float, default: 1e-4

      Parameter for Armijo condition rule.

    c2 float, default: 0.9

      Parameter for curvature condition rule.

    hess_inv0 None or ndarray, optional``

      Initial inverse hessian estimate, shape (n, n). If None (default) then the identity matrix is used.

  Returns:
    xopt ndarray

      Parameters which minimize f, i.e., `f(xopt) == fopt`.

    fopt float

      Minimum value.

    gopt ndarray

      Value of gradient at minimum, f’(xopt), which should be near 0.

    Bopt ndarray

      Value of 1/f’’(xopt), i.e., the inverse Hessian matrix.

    func_calls int

      Number of function_calls made.

    grad_calls int

      Number of gradient calls made.

    warnflag int

      1 : Maximum number of iterations exceeded. 2 : Gradient and/or function calls not changing. 3 : NaN result encountered.

    allvecs list

      The value of xopt at each iteration. Only returned if retall is True.

  See also

  `minimize`

    Interface to minimization algorithms for multivariate functions. See `method='BFGS'` in particular.

  Notes

  Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS).

  Parameters c1 and c2 must satisfy `0 < c1 < c2 < 1`.

  References

  [1]

  Wright, and Nocedal ‘Numerical Optimization’, 1999, p. 198.

  Examples

      >>> import numpy as np
      >>> from scipy.optimize import fmin_bfgs
      >>> def quadratic_cost(x, Q):
      ...     return x @ Q @ x
      ...
      >>> x0 = np.array([-3, -4])
      >>> cost_weight =  np.diag([1., 10.])
      >>> # Note that a trailing comma is necessary for a tuple with single element
      >>> fmin_bfgs(quadratic_cost, x0, args=(cost_weight,))
      Optimization terminated successfully.
              Current function value: 0.000000
              Iterations: 7                   # may vary
              Function evaluations: 24        # may vary
              Gradient evaluations: 8         # may vary
      array([ 2.85169950e-06, -4.61820139e-07])

      >>> def quadratic_cost_grad(x, Q):
      ...     return 2 * Q @ x
      ...
      >>> fmin_bfgs(quadratic_cost, x0, quadratic_cost_grad, args=(cost_weight,))
      Optimization terminated successfully.
              Current function value: 0.000000
              Iterations: 7
              Function evaluations: 8
              Gradient evaluations: 8
      array([ 2.85916637e-06, -4.54371951e-07])