class_docstr: Jennrich-Sampson objective function.
  
  This class defines the Jennrich-Sampson [1]_ global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{JennrichSampson}}(x) = \sum_{i=1}^{10} \left [2 + 2i
      - (e^{ix_1} + e^{ix_2}) \right ]^2
  
  
  with :math:`x_i \in [-1, 1]` for :math:`i = 1, 2`.
  
  *Global optimum*: :math:`f(x) = 124.3621824` for
  :math:`x = [0.257825, 0.257825]`.
  
  .. [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 __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-1.0] * self.N, [1.0] * self.N))
  
      self.global_optimum = [[0.257825, 0.257825]]
      self.custom_bounds = [(-1, 0.34), (-1, 0.34)]
      self.fglob = 124.3621824
  
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 1 to create a new JennrichSampson object from inherited base classes: Benchmark with example: obj = JennrichSampson(dimensions)