class_docstr: Dixon and Price objective function.
  
  This class defines the Dixon and Price global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{DixonPrice}}(x) = (x_i - 1)^2
      + \sum_{i=2}^n i(2x_i^2 - x_{i-1})^2
  
  
  Here, :math:`n` represents the number of dimensions and
  :math:`x_i \in [-10, 10]` for :math:`i = 1, ..., n`.
  
  *Global optimum*: :math:`f(x_i) = 0` for
  :math:`x_i = 2^{- \frac{(2^i - 2)}{2^i}}` for :math:`i = 1, ..., n`
  
  .. [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.
  
  TODO: Gavana code not correct.  i array should start from 2.
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-10.0] * self.N, [10.0] * self.N))
      self.custom_bounds = [(-2, 3), (-2, 3)]
  
      self.global_optimum = [
          [
              2.0 ** (-(2.0**i - 2.0) / 2.0**i)
              for i in range(1, self.N + 1)
          ]
      ]
      self.fglob = 0.0
      self.change_dimensionality = True
  
imports: import numpy
  from numpy import abs, cos, exp, arange, pi, sin, sqrt, sum, zeros, tanh
  from numpy.testing import assert_almost_equal
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new DixonPrice object from inherited base classes: Benchmark with example: obj = DixonPrice(dimensions)