input: from typing import List
  
  
  def optimize_objective_function(dimension: int, initial_guess: List[float], method: str, options: dict) -> List[float]:
      """
      Optimize the given objective function using the provided optimization method and options.
      >>> optimize_objective_function(3, [0.0, 0.0, 0.0], 'nelder-mead', {'xtol': 1e-8, 'disp': True})
      [optimized_x1, optimized_x2, optimized_x3]
      >>> optimize_objective_function(2, [1.0, 1.0], 'bfgs', {'gtol': 1e-5, 'disp': False})
      [optimized_x1, optimized_x2]
      """
      def obj_func(x):
          # Placeholder for the objective function
          return (x[0] - 1)**2 + (x[1] - 2.5)**2
      
      from scipy.optimize import minimize
      
      res = minimize(obj_func, initial_guess, method=method, options=options)
      return res.x
output:     def obj_func(x):
          # Placeholder for the objective function
          return (x[0] - 1)**2 + (x[1] - 2.5)**2
      
      from scipy.optimize import minimize
      
      res = minimize(obj_func, initial_guess, method=method, options=options)
      return res.x