Respond to two other classmates’ postings by critically reviewing your classmate's answer. The question: Differentiate between Big O, big Omega(Ω) and big theta, and which one you think is better to measure your algorithm efficiency, justify? 1st Answer: The better way to measure an algorithm is Big O because, in the worst-case scenario, we determine an algorithm's upper bound on execution time. We need to understand the scenario in which most operations are carried out. The worst scenario for a linear search is when an element x to be searched, is empty from the array. When x is empty, the search() method compares it with elements in arr[] one by one. Then that will show us the worst case of the algorithm which makes it easier to compare it to other classifications. 2nd Answer: The better way to measure an algorithm is Big O because we use big O notation is important when working with algorithms. We can develop efficient algorithms which can lead to less code(space complexity) and faster programs, Big-O notation measures how fast the running time of an algorithm grows with the input. ( critically review on these two answers )
Respond to two other classmates’ postings by critically reviewing your classmate's answer.
The question: Differentiate between Big O, big Omega(Ω) and big theta, and which one you think is better to measure your
1st Answer:
The better way to measure an algorithm is Big O because, in the worst-case scenario, we determine an algorithm's upper bound on execution time. We need to understand the scenario in which most operations are carried out. The worst scenario for a linear search is when an element x to be searched, is empty from the array. When x is empty, the search() method compares it with elements in arr[] one by one. Then that will show us the worst case of the algorithm which makes it easier to compare it to other classifications.
2nd Answer:
The better way to measure an algorithm is Big O because we use big O notation is important when working with algorithms. We can develop efficient algorithms which can lead to less code(space complexity) and faster programs, Big-O notation measures how fast the running time of an algorithm grows with the input.
( critically review on these two answers )
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