class_docstr: Goldstein-Price objective function.
  
  This class defines the Goldstein-Price [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{GoldsteinPrice}}(x) = \left[ 1 + (x_1 + x_2 + 1)^2 
      (19 - 14 x_1 + 3 x_1^2 - 14 x_2 + 6 x_1 x_2 + 3 x_2^2) \right]
      \left[ 30 + ( 2x_1 - 3 x_2)^2 (18 - 32 x_1 + 12 x_1^2
      + 48 x_2 - 36 x_1 x_2 + 27 x_2^2) \right]
  
  
  with :math:`x_i \in [-2, 2]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = 3` for :math:`x = [0, -1]`
  
  .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
  For Global Optimization Problems Int. Journal of Mathematical Modelling
  and Numerical Optimisation, 2013, 4, 150-194.
code: def fun(self, x, *args):
      self.nfev += 1
  
      a = 1 + (x[0] + x[1] + 1) ** 2 * (
          19
          - 14 * x[0]
          + 3 * x[0] ** 2
          - 14 * x[1]
          + 6 * x[0] * x[1]
          + 3 * x[1] ** 2
      )
      b = 30 + (2 * x[0] - 3 * x[1]) ** 2 * (
          18
          - 32 * x[0]
          + 12 * x[0] ** 2
          + 48 * x[1]
          - 36 * x[0] * x[1]
          + 27 * x[1] ** 2
      )
      return a * b
  
imports: import numpy
  from numpy import abs, sin, cos, exp, floor, log, arange, prod, sqrt, sum
  from .go_benchmark import Benchmark
label_desc: Use image node_id 2 for calling the GoldsteinPrice obj's underlying member method code with example usage: obj.fun(x) and returns: unknown