class_docstr: Judge objective function.
  
  This class defines the Judge [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Judge}}(x) = \sum_{i=1}^{20}
      \left [ \left (x_1 + A_i x_2 + B x_2^2 \right ) - C_i \right ]^2
  
  
  Where, in this exercise:
  
  .. math::
  
      \begin{cases}
      C = [4.284, 4.149, 3.877, 0.533, 2.211, 2.389, 2.145,
      3.231, 1.998, 1.379, 2.106, 1.428, 1.011, 2.179, 2.858, 1.388, 1.651,
      1.593, 1.046, 2.152] \\
      A = [0.286, 0.973, 0.384, 0.276, 0.973, 0.543, 0.957, 0.948, 0.543,
           0.797, 0.936, 0.889, 0.006, 0.828, 0.399, 0.617, 0.939, 0.784,
           0.072, 0.889] \\
      B = [0.645, 0.585, 0.310, 0.058, 0.455, 0.779, 0.259, 0.202, 0.028,
           0.099, 0.142, 0.296, 0.175, 0.180, 0.842, 0.039, 0.103, 0.620,
           0.158, 0.704]
      \end{cases}
  
  
  with :math:`x_i \in [-10, 10]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x_i) = 16.0817307` for
  :math:`\mathbf{x} = [0.86479, 1.2357]`.
  
  .. [1] Gavana, A. Global Optimization Benchmarks and AMPGO retrieved 2015
code: def fun(self, x, *args):
      self.nfev += 1
  
      return sum(
          ((x[0] + x[1] * self.a + (x[1] ** 2.0) * self.b) - self.c)
          ** 2.0
      )
  
file_path: scipy/benchmarks/benchmarks/go_benchmark_functions/go_funcs_J.py
imports: from numpy import sum, asarray, arange, exp
  from .go_benchmark import Benchmark
label_desc: Use image node_id 2 for calling the Judge obj's underlying member method code with example usage: obj.fun(x) and returns: sum