Consider the implementation of insertion sort that uses a modified binary search algorithm below: def insertion_sort(arr): for i in range(1, len(arr)): t = arr[i] # get index of arr[i] in # assumed sorted sub-array arr[:i] j = binary_search(arr[i], arr[:i]) # insert arr[i] at index j arr.insert(j, arr[i]) # removes element at index i + 1 # (shifted because of insert) arr.pop(i+1) Assuming "arr" is a dynamic list and binary_search runs in O(log i)) time, what is the worst- case time complexity of this specific implementation of insertion sort? Briefly defend your answer. How does it differ from the previously implemented insertion sort algorithm that doesn't use binary search? How did this affect the time complexity?
Consider the implementation of insertion sort that uses a modified binary search algorithm below: def insertion_sort(arr): for i in range(1, len(arr)): t = arr[i] # get index of arr[i] in # assumed sorted sub-array arr[:i] j = binary_search(arr[i], arr[:i]) # insert arr[i] at index j arr.insert(j, arr[i]) # removes element at index i + 1 # (shifted because of insert) arr.pop(i+1) Assuming "arr" is a dynamic list and binary_search runs in O(log i)) time, what is the worst- case time complexity of this specific implementation of insertion sort? Briefly defend your answer. How does it differ from the previously implemented insertion sort algorithm that doesn't use binary search? How did this affect the time complexity?
Computer Networking: A Top-Down Approach (7th Edition)
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Topic:
PYTHON
Big-O NOTATION
Linked Lists in Python
![ΡΥTHON
Big-O Notation
Linked Lists
Consider the implementation of insertion sort that uses a modified binary search algorithm
below:
def insertion_sort(arr):
for i in range (1, len(arr)):
t = arr[i]
# get index of arr[i] in
# assumed sorted sub-array arr[:i]
j = binary_search(arr[i], arr[:i])
# insert arr[i] at index j
arr.insert(j, arr[i])
# removes element at index i + 1
# (shifted because of insert)
arr.pop(i+1)
Assuming "arr" is a dynamic list and binary_search runs in O(log i)) time, what is the worst-
case time complexity of this specific implementation of insertion sort? Briefly defend your
answer. How does it differ from the previously implemented insertion sort algorithm that
doesn't use binary search? How did this affect the time complexity?](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F02c68de7-6210-4380-8de5-231632625aeb%2Fc0675087-1d3a-406a-8f20-cad228a34832%2Fi0s1jz_processed.png&w=3840&q=75)
Transcribed Image Text:ΡΥTHON
Big-O Notation
Linked Lists
Consider the implementation of insertion sort that uses a modified binary search algorithm
below:
def insertion_sort(arr):
for i in range (1, len(arr)):
t = arr[i]
# get index of arr[i] in
# assumed sorted sub-array arr[:i]
j = binary_search(arr[i], arr[:i])
# insert arr[i] at index j
arr.insert(j, arr[i])
# removes element at index i + 1
# (shifted because of insert)
arr.pop(i+1)
Assuming "arr" is a dynamic list and binary_search runs in O(log i)) time, what is the worst-
case time complexity of this specific implementation of insertion sort? Briefly defend your
answer. How does it differ from the previously implemented insertion sort algorithm that
doesn't use binary search? How did this affect the time complexity?
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