This content originally appeared on DEV Community and was authored by DEV Community
What is Big O notation?
Big O notation is used to measure how the running time or space requirements for our program grows as input grows.
- Measuring running time growth is time complexity. (In this post we focus on time complexity)
- Measuring space growth is space complexity.
Rules to determine time complexity
- Keep fastest growing term.
- Drop constants.
Big O refers to very large value of 'n' (n = size) where time, t = an^2 + bn + c.
For example, if we have a function like,
t = 5n^2 + 3n + 20
When value of 'n' is very large, bn+c becomes irrelevant.
ie; an^2 is very larger than bn+c.
For example; if n = 1000 then,
t = 5 * 1000^2 + 3*1000+20 = 5000000 + 3020; where 3020, a small value becomes irrelevant.
Different time complexities
Here we discuss about a few of them.
1. O(n)
def foo(arr): size(arr) -> 100 -> 0.22ms
def foo(arr): size(arr) -> 1000 -> 2.30ms
Here time increases as array size increases; time proportional to size(arr).
n = size(arr), b= constant
t = a*n + b
- Keep fastest growing term => t = a * n (b is constant)
- Drop constants => t = n ( a is constant) Therefore; t = O(n)
Example program for t = O(n): To get square numbers
def get_squared_numbers(numbers):
squared_numbers = []
for n in numbers:
square_numbers.append(n * n)
return squared_numbers
numbers = [2, 5, 8, 9]
get_squared_numbers(numbers)
# returns [4, 25, 64, 81]
2. O(1)
def foo(arr): size(arr) -> 100 -> 0.22ms
def foo(arr): size(arr) -> 1000 -> 0.23ms
time, t = a * n + b -> t = a * n ( b constant) -> t = n (dropping constants(a))
n = 1 => t = 1
Therefore, t = O(1)
Example program for t = O(1)
def find_first_pe(prices, eps, index):
pe = prices[index]/eps[index]
return pe
3. O(n^2)
Example Program for O(n^2): To find duplicates from the list
numbers = [3, 6, 2, 4, 3, 6, 8, 9]
for i in range(len(numbers)):
for j in range(i + 1, len(numbers)):
if numbers[i] == numbers[j]:
print(numbers[i] + " is a duplicate.")
break
Also there is O(log n), O(2^n) time complexities.
This content originally appeared on DEV Community and was authored by DEV Community
DEV Community | Sciencx (2022-03-14T08:20:40+00:00) Big O notation explained: DSA-01. Retrieved from https://www.scien.cx/2022/03/14/big-o-notation-explained-dsa-01/
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