Algorithms in Python | Binary Search | Full Code | Telugu

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Algorithms in Python | Binary Search | Full Code with Time Complexity explanation| Telugu

Time Code:
00:00 Intro
04:16 Python Code
09:00 Integer Overflow
11:00 Document Strings
13:00 Time Complexity log n

Integer Overflow - https://en.wikipedia.org/wiki/Integer_overflow


Binary Search
list_num = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]
item = 2


def bin_search(sequence, item):
"""
This function requires two position parameters
sequence-- list of sorted numbers &
item -- for the value to search

Then this function returns the index position if the item is found
else returns None
"""
begin = 0
end = len(sequence) - 1

while begin LT= end:
# to avoid integer over flow
mid = begin + (end - begin) // 2
if sequence[mid] == item:
return mid
elif sequence[mid] GT item:
end = mid - 1
else:
begin = mid + 1
return None


TIME COMPLEXITY BINARY SEARCH
----------------------------------------------------------
= n x 1/2 x 1/2 x 1/2 x 1/2 x 1/2 x 1/2 x ½…..1/2k for k items

c= n/2k


If we solve for k =

• n = 2k Taking Log on both sides

• log n = log 2k
• k =log2n which is logarithmic time complexity

Time complexity is the computational complexity that describes the amount of computer time it takes to run an algorithm. Time complexity is commonly estimated by counting the number of elementary operations performed by the algorithm

Time Complexity is commonly expressed using big O notation, typically O(n), O (logn), O(n^2), O(2^n), O(n!) etc where n is the input size in units of bits needed to represent the input.

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