input: Write a python function to solve a linear optimization problem using scipy.optimize.minimize.
  assert optimize_problem() == {'fun': 0.0, 'jac': array([5., 6.]), 'message': 'Optimization terminated successfully', 'nfev': 3, 'nit': 2, 'njev': 1, 'status': 0, 'success': True, 'x': array([ 0.,  0.])}
output: def optimize_problem():
      from scipy.optimize import minimize
      import numpy as np
      
      # Define the objective function
      def objective(x):
          return 5*x[0] + 6*x[1]
      
      # Define the constraints
      def constraint1(x):
          return x[0] + x[1] - 1
  
      # Define bounds for the variables
      b = (0.0, None)
      bounds = [b, b]
  
      # Initial guess
      x0 = np.array([0.5, 0.5])
      
      # Define the constraints dictionary
      con1 = {'type': 'eq', 'fun': constraint1}
      cons = [con1]
  
      # Perform the optimization
      solution = minimize(objective, x0, method='SLSQP', bounds=bounds, constraints=cons)
      
      return {
          'fun': solution.fun,
          'jac': solution.jac,
          'message': solution.message,
          'nfev': solution.nfev,
          'nit': solution.nit,
          'njev': solution.njev,
          'status': solution.status,
          'success': solution.success,
          'x': solution.x
      }