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# scipy.optimize.line_search

`scipy.optimize.``line_search`(f, myfprime, xk, pk, gfk, old_fval, old_old_fval, args=(), c1=0.0001, c2=0.90000000000000002, amax=50)

  Find alpha that satisfies strong Wolfe conditions.

  Parameters: | f : callable f(x,*args) Objective function. myfprime : callable f’(x,*args) Objective function gradient (can be None). xk : ndarray Starting point. pk : ndarray Search direction. gfk : ndarray Gradient value for x=xk (xk being the current parameter estimate). args : tuple Additional arguments passed to objective function. c1 : float Parameter for Armijo condition rule. c2 : float Parameter for curvature condition rule.
  Returns: | alpha0 : float Alpha for which `x_new = x0 + alpha * pk`. fc : int Number of function evaluations made. gc : int Number of gradient evaluations made.
  Notes: | Uses the line search algorithm to enforce strong Wolfe conditions. See Wright and Nocedal, ‘Numerical Optimization’, 1999, pg. 59-60. For the zoom phase it uses an algorithm by [...].

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