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Time Complexity

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Asymptotic notations

Asymptotic notations are mathematical notations which are used to describe the running time of an algorithm when input tends towards infinity.

For example, in the last section, we discussed an example where we had to find ‘1’ in the array. We saw that when the input array had ‘1’ in its first position the time taken by the algorithm was constant, whereas when the array had ‘1’ in its last position or did not have ‘1’ at all, the time taken by the algorithm was linear.

There are mainly 3 types of Asymptotic notations:

1. Big-O notation: The Big-O notation describes the worst-case running time of an algorithm. It is computed by counting the number of operations it will take in the worst-case scenario with the input ‘n’.

O(g(n)) = { f(n): there exist positive constants c and n0
           such that 0 ≤ f(n) ≤ cg(n) for all n ≥ n0 }

2. Big Omega() notation: The notation describes the best running time of an algorithm. It is computed by counting the number of operations it will take in the best-case scenario with the input ‘n’.

Ω(g(n)) = { f(n): there exist positive constants c and n0 
           such that 0 ≤ cg(n) ≤ f(n) for all n ≥ n0 }

3. Big Theta() Notation: The theta notation encloses the function from above and below, therefore it defines the exact asymptotic behaviour. The notation is used for analyzing the average runtime of an algorithm.

Θ(g(n)) = { f(n): there exist positive constants c1, c2 and n0
           such that 0 ≤ c1g(n) ≤ f(n) ≤ c2g(n) for all n ≥ n0 }

It’s important to note here that O, and are not functions. For example, O(n) represents the class of all functions that grow at most as quickly as the linear function f(n)=n.

Big-O notations give us a convenient way to talk about upper bounds. For example, we can say the time complexity of the algorithm is O(n^3) ( i.e. T(n)  O(n^3) ), which means that the running time of the algorithm is at most cubic.

Another point to note here is that running time and time complexity are two different things, for example, if the running time of an algorithm is the following T(n)= 3*n^2 + 4*n + 2, the time complexity would be O(n^2). 

Types of Time Complexity

Some Common Algorithmic Runtimes. (from fastest to slowest)

O(1) Constant Time Complexity Example: Sum of two numbers.
O(logn) Logarithmic Time Complexity Example: Finding an element in a sorted array by using binary search.
O(n) Linear Time Complexity Example: Finding the sum of an array of size n.
O(n logn) Log-Linear Time Complexity Example: Sorting the array using merge sort.
O(n2) Quadratic Time Complexity Example: Finding the sum of every pair of elements in an array.
O(2n) Exponential Time Complexity Example: Finding all the subsets.
O(n!) Factorial Time Complexity Example: Finding all the permutations of a given array.

Time Complexity Cheat Sheet

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Time Complexity Problems

Basic primer
Problem Score Companies Time Status
LOOP_CMPL 20
2:43
NESTED_CMPL 20
1:10
NESTED_CMPL2 30
1:25
CHOOSE4 50
0:57
Math
Problem Score Companies Time Status
WHILE_CMPL 50
1:31
NESTED_CMPL3 80 3:56
LOOP_CMPL2 80
2:43
GCD_CMPL 150
4:13
Compare functions
Problem Score Companies Time Status
CHOOSE3 50
1:39
CHOOSE1 50
1:43
CHOOSE2 80
2:23
Function calling itself
Problem Score Companies Time Status
REC_CMPL1 80
6:58
REC_CMPL2 80
6:25
REC_CMPL3 150
4:39
Amortized complexity
Problem Score Companies Time Status
AMORTIZED1 100
3:03

Additional Practice

Problem Score Companies Time Status
Collatz Conjecture 200 23:04
Palindromic Time 200 40:04
Pangram Check 100 27:13
Climbing Stairs 150 29:10
Integers in Strings 100 20:49
Word Count 150 23:09
Extracting Numbers 100
LTI
24:49
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