Stacks_2
See the Stack ADT implemented using a Python's List, this ADT was discussed in lectures. The pop method's amortized run time complexity is O(____).

Question

Stacks_2
See the Stack ADT implemented using a Python's List, this ADT was discussed in lectures. The pop method's amortized run time complexity is  O(____).

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Question restatement: Stacks_2 See the Stack ADT implemented using a Python's List, this ADT was discussed in lectures. The pop method's amortized run time complexity is  O(____).
Answer options: ["1", "n", "nlogn", "logn", "n2"]

Option 1: "1". This corresponds to O(1) time. In Python, popping from the end of a list (which is what a stack pop does) is an amortized constant-time operation because it simply removes the last element and adjusts the size counter, with the underlying memory management designed to handle such end removals efficiently.

Option 2: "n". Claiming O(n) would suggest the operation scales linearly with the number of elements in the stack. While some operations on a list could be linear in the worst case, popping from the end does not generally require traversing the list, so this is not correct for the typical stack pop.

Option 3: "nlogn". O(n log n) would imply a complexity that grows faster than linear by a logarithmic factor. There is no logarithmic factor involved in a simple end-pop on a Python list, so this is incorrect.

Option 4: "logn". O(log n) indicates logarithmic growth, which is typical for binary search or certain balanced-tree operations, not for popping from the end of a Python list. Therefore this is not appropriate here.

Option 5: "n2". O(n^2) suggests a quadratic growth, which would occur if nested linear-time steps happened per operation. A single end-pop on a Python list does not exhibit this behavior, so this is incorrect.

Stacks_2 See the Stack ADT implemented using a Python's List, this ADT was discussed in lectures. The pop method's amortized run time complexity is O(____).

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