input: Write a Python function to minimize a linear objective function subject to inequality constraints using scipy.optimize.minimize.
  assert minimize_objective_function([0, 0]) == 0.0
  assert minimize_objective_function([1, 1]) == 10.0
output: def minimize_objective_function(initial_guess):
      from scipy.optimize import minimize
  
      # Objective function: f(x) = 5*x[0] + 5*x[1]
      def objective_function(x):
          return 5*x[0] + 5*x[1]
  
      # Inequality constraints: x[0] + x[1] >= 1, x[0] + x[1] <= 3
      constraints = (
          {'type': 'ineq', 'fun': lambda x: x[0] + x[1] - 1},
          {'type': 'ineq', 'fun': lambda x: 3 - (x[0] + x[1])}
      )
  
      # Bounds for variables x[0] and x[1]
      bounds = [(0, None), (0, None)]
  
      # Minimize the objective function
      result = minimize(objective_function, initial_guess, bounds=bounds, constraints=constraints)
  
      return result.fun