#Cost-Parametric Linear Programming



    # Define the variables

    # Define the objective function

    # Define the constraints

    # Create the problem

    # Set the cost parameter value

    # Solve the problem

    # Print the solution


# Example usage

if __name__ == '__main__':
    import cvxpy as cp
    import numpy as np
    def solve_cost_parametric_linear_programming(cost_parameter):
        x = cp.Variable(2)
        c = cp.Parameter(2)  # Parameterized cost coefficients
        objective = cp.Minimize(c.T @ x)
        constraints = [x >= 0, x[0] + x[1] <= 1]
        problem = cp.Problem(objective, constraints)
        c.value = cost_parameter
        problem.solve()
        if problem.status == "optimal":
            print("Optimal solution found for cost parameter =", cost_parameter)
            print("x =", x.value)
            print("Objective =", problem.value)
        else:
            print("No feasible solution found for cost parameter =", cost_parameter)
    if __name__ == "__main__":
        cost_parameter = 1.0  # Example cost parameter
        solve_cost_parametric_linear_programming(cost_parameter)
