class_docstr: Trid objective function.
  
  This class defines the Trid [1]_ global optimization problem. This is a
  multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Trid}}(x) = \sum_{i=1}^{n} (x_i - 1)^2
                          - \sum_{i=2}^{n} x_i x_{i-1}
  
  
  Here, :math:`n` represents the number of dimensions and
  :math:`x_i \in [-20, 20]` for :math:`i = 1, ..., 6`.
  
  *Global optimum*: :math:`f(x) = -50` for :math:`x = [6, 10, 12, 12, 10, 6]`
  
  .. [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 Jamil#150, starting index of second summation term should be 2.
code: def fun(self, x, *args):
      self.nfev += 1
  
      return sum((x - 1.0) ** 2.0) - sum(x[1:] * x[:-1])
  
imports: from numpy import abs, asarray, cos, exp, arange, pi, sin, sum, atleast_2d
  from .go_benchmark import Benchmark
label_desc: Use image node_id 2 for calling the Trid obj's underlying member method code with example usage: obj.fun(x) and returns: unknown