I get the concept of big 0, big theta, and big omega notation but I'm just not sure about how to tackle these problems. A couple tips and an answer to the problem would be really helpful/

Big-O Notation When trying to define the efficiency of an algorithm in terms of its execution time, we are in need of a systematical way of comparing the performance of one algorithm to another independent of any particular computer system. This way we can ensure that we are comparing the performance of the actual algorithm as opposed to the speed of the computer system. To accomplish this task, it is important to quantify the number of steps that an algorithm must take in order to complete its task. When quantified, these number of steps can be considered the execution time of the algorithm. In computer science, while determining the number of steps that an algorithm must take to complete its tasks is important, it is not as important as determining which part of the algorithm is the most dominant. The Big-O function (also known as the order of magnitude function) describes the parts of an operation that increase the fastest as the problem grows and is written as Off(n)). This dominant part of the program can be used to compare algorithms to another. It provides a handy approximation of how many steps an algorithm must take to complete its task. Question Suppose that the exact number of steps for some algorithm is defined by the function: T(n) = 4n2 + 18n + 705 What is the Big O, Big Omega, and Big Theta of T(n)? Fill in each function so that it returns the specified complexity analysis. For example, if the Big O of the function was n you would write "return n" Fill in each function so that it returns the specified complexity analysis. For example, if the Big O of the function was n you would write "return n" v Grading 3 Run Tests code.py New Full Screen from math import log, factorial #if the functi. 3- def big_o_complexity(n): Check Big O 0 /1 pts - Click for details FAILED B Return the Big O complexity of the code in return log(n) Check Big Omega 0/1 pts - Click for details FAILED 0 def big_omega_complexity(n): Check Big Theta 0 /1 pts - Click for details FAILED B Return the Big Omega complexity of the cod return None 15. def big_theta_complexity(n): Return the Big Theta complexity of the cod