class_docstr: Line search algorithm which iteratively optimizes the value $f(x)$ for $x$ on the line between
  $x'$ and $x_0$ by optimistically taking the first acceptable $x$ starting from $x_0$ and
  moving towards $x'$.
class_docstr_tok: ["Line","search","algorithm","which","iteratively","optimizes","the","value","$","f","(","x",")","$","for","$","x","$","on","the","line","between","$","x","'","$","and","$","x_0","$","by","optimistically","taking","the","first","acceptable","$","x","$","starting","from","$","x_0","$","and","moving","towards","$","x","'","$","."]
docstr: Create a new line search solver instance.
  
  Args:
      max_iterations (parameter, int >= 1): Maximum number of iterations before termination.
      backtracking_factor (parameter, 0.0 < float < 1.0): Backtracking factor.
docstr_tok: ["Create","a","new","line","search","solver","instance",".","Args",":","max_iterations","(","parameter",",","int",">","=","1",")",":","Maximum","number","of","iterations","before","termination",".","backtracking_factor","(","parameter",",","0.0","<","float","<","1.0",")",":","Backtracking","factor","."]
code: def __init__(self, *, name, max_iterations, backtracking_factor):
      """
      Create a new line search solver instance.
  
      Args:
          max_iterations (parameter, int >= 1): Maximum number of iterations before termination.
          backtracking_factor (parameter, 0.0 < float < 1.0): Backtracking factor.
      """
      super().__init__(name=name, max_iterations=max_iterations)
  
      self.backtracking_factor = self.submodule(
          name="backtracking_factor",
          module=backtracking_factor,
          modules=parameter_modules,
          dtype="float",
          min_value=0.0,
          max_value=1.0,
      )
  
file_path: tensorforce/tensorforce/core/optimizers/solvers/line_search.py
imports: import tensorflow
  from tensorforce import util
  from tensorforce.core import parameter_modules, SignatureDict, TensorSpec, tf_function, tf_util
  from tensorforce.core.optimizers.solvers import Iterative
label_desc: Use image node_id 1 to create a new LineSearch object from inherited base classes: Iterative with example: obj = LineSearch()