class_docstr: Venter Sobiezcczanski-Sobieski objective function.
  
  This class defines the Venter Sobiezcczanski-Sobieski [1]_ global optimization
  problem. This is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{VenterSobiezcczanskiSobieski}}(x) = x_1^2 - 100 \cos^2(x_1)
                                                    - 100 \cos(x_1^2/30)
                                                    + x_2^2 - 100 \cos^2(x_2)
                                                    - 100 \cos(x_2^2/30)
  
  
  with :math:`x_i \in [-50, 50]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = -400` for :math:`x = [0, 0]`
  
  .. [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 #160 hasn't written the equation very well. Normally a cos
  squared term is written as cos^2(x) rather than cos(x)^2
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-50.0] * self.N, [50.0] * self.N))
      self.custom_bounds = ([-10, 10], [-10, 10])
  
      self.global_optimum = [[0.0 for _ in range(self.N)]]
      self.fglob = -400
  
file_path: scipy/benchmarks/benchmarks/go_benchmark_functions/go_funcs_V.py
imports: from numpy import sum, cos, sin, log
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new VenterSobiezcczanskiSobieski object from inherited base classes: Benchmark with example: obj = VenterSobiezcczanskiSobieski(dimensions)