Time Complexity and Space Complexity
Generally, there is always more than one way to solve a problem in computer science with different algorithms. Therefore, it is highly required to use a method to compare the solutions in order to judge which one is more optimal. The method must be:
- Independent of the machine and its configuration, on which the algorithm is running on.
- Shows a direct correlation with the number of inputs.
- Can distinguish two algorithms clearly without ambiguity.
There are two such methods used, time complexity and space complexity which are discussed below:
Time Complexity: The time complexity of an algorithm quantifies the amount of time taken by an algorithm to run as a function of the length of the input. Note that the time to run is a function of the length of the input and not the actual execution time of the machine on which the algorithm is running on.
In order to calculate time complexity on an algorithm, it is assumed that a constant time c is taken to execute one operation, and then the total operations for an input length on N are calculated. Consider an example to understand the process of calculation: Suppose a problem is to find whether a pair (X, Y) exists in an array, A of N elements whose sum is Z. The simplest idea is to consider every pair and check if it satisfies the given condition or not.
The pseudo-code is as follows:
int a[n]; for(int i = 0;i < n;i++) cin >> a[i] for(int i = 0;i < n;i++) for(int j = 0;j < n;j++) if(i!=j && a[i]+a[j] == z) return true return false
Below is the implementation of the above approach:
C++
// C++ program for the above approach #include <bits/stdc++.h> using namespace std; // Function to find a pair in the given // array whose sum is equal to z bool findPair( int a[], int n, int z) { // Iterate through all the pairs for ( int i = 0; i < n; i++) for ( int j = 0; j < n; j++) // Check if the sum of the pair // (a[i], a[j]) is equal to z if (i != j && a[i] + a[j] == z) return true ; return false ; } // Driver Code int main() { // Given Input int a[] = { 1, -2, 1, 0, 5 }; int z = 0; int n = sizeof (a) / sizeof (a[0]); // Function Call if (findPair(a, n, z)) cout << "True" ; else cout << "False" ; return 0; } |
Java
// Java program for the above approach import java.lang.*; import java.util.*; class GFG{ // Function to find a pair in the given // array whose sum is equal to z static boolean findPair( int a[], int n, int z) { // Iterate through all the pairs for ( int i = 0 ; i < n; i++) for ( int j = 0 ; j < n; j++) // Check if the sum of the pair // (a[i], a[j]) is equal to z if (i != j && a[i] + a[j] == z) return true ; return false ; } // Driver code public static void main(String[] args) { // Given Input int a[] = { 1 , - 2 , 1 , 0 , 5 }; int z = 0 ; int n = a.length; // Function Call if (findPair(a, n, z)) System.out.println( "True" ); else System.out.println( "False" ); } } // This code is contributed by avijitmondal1998 |
Python3
# Python3 program for the above approach # Function to find a pair in the given # array whose sum is equal to z def findPair(a, n, z) : # Iterate through all the pairs for i in range (n) : for j in range (n) : # Check if the sum of the pair # (a[i], a[j]) is equal to z if (i ! = j and a[i] + a[j] = = z) : return True return False # Driver Code # Given Input a = [ 1 , - 2 , 1 , 0 , 5 ] z = 0 n = len (a) # Function Call if (findPair(a, n, z)) : print ( "True" ) else : print ( "False" ) # This code is contributed by splevel62. |
C#
// C# program for above approach using System; class GFG{ // Function to find a pair in the given // array whose sum is equal to z static bool findPair( int [] a, int n, int z) { // Iterate through all the pairs for ( int i = 0; i < n; i++) for ( int j = 0; j < n; j++) // Check if the sum of the pair // (a[i], a[j]) is equal to z if (i != j && a[i] + a[j] == z) return true ; return false ; } // Driver Code static void Main() { // Given Input int [] a = { 1, -2, 1, 0, 5 }; int z = 0; int n = a.Length; // Function Call if (findPair(a, n, z)) Console.WriteLine( "True" ); else Console.WriteLine( "False" ); } } // This code is contributed by sanjoy_62. |
Javascript
<script> // JavaScript program for the above approach // Function to find a pair in the given // array whose sum is equal to z function findPair(a, n, z) { // Iterate through all the pairs for (let i = 0; i < n; i++) for (let j = 0; j < n; j++) // Check if the sum of the pair // (a[i], a[j]) is equal to z if (i != j && a[i] + a[j] == z) return true ; return false ; } // Driver Code // Given Input let a = [ 1, -2, 1, 0, 5 ]; let z = 0; let n = a.length; // Function Call if (findPair(a, n, z)) document.write( "True" ); else document.write( "False" ); // This code is contributed by code_hunt </script> |
False
Assuming that each of the operations in the computer takes approximately constant time, let it be c. The number of lines of code executed actually depends on the value of Z. During analyses of the algorithm, mostly the worst-case scenario is considered, i.e., when there is no pair of elements with sum equals Z. In the worst case,
- N*c operations are required for input.
- The outer loop i loop runs N times.
- For each i, the inner loop j loop runs N times.
So total execution time is N*c + N*N*c + c. Now ignore the lower order terms since the lower order terms are relatively insignificant for large input, therefore only the highest order term is taken (without constant) which is N*N in this case. Different notations are used to describe the limiting behavior of a function, but since the worst case is taken so big-O notation will be used to represent the time complexity.
Hence, the time complexity is O(N^{2}) for the above algorithm. Note that the time complexity is solely based on the number of elements in array A i.e the input length, so if the length of the array will increase the time of execution will also increase.
Order of growth is how the time of execution depends on the length of the input. In the above example, it is clearly evident that the time of execution quadratically depends on the length of the array. Order of growth will help to compute the running time with ease.
Another Example: Let’s calculate the time complexity of the below algorithm:
C++
count = 0 for ( int i = N; i > 0; i /= 2) for ( int j = 0; j < i; j++) count++; |
This is a tricky case. In the first look, it seems like the complexity is O(N * log N). N for the j′s loop and log(N) for i′s loop. But it’s wrong. Let’s see why.
Think about how many times count++ will run.
- When i = N, it will run N times.
- When i = N / 2, it will run N / 2 times.
- When i = N / 4, it will run N / 4 times.
- And so on.
The total number of times count++ will run is N + N/2 + N/4+…+1= 2 * N. So the time complexity will be O(N).
Some general time complexities are listed below with the input range for which they are accepted in competitive programming:
Input Length | Worst Accepted Time Complexity | Usually type of solutions |
10 -12 | O(N!) | |
15-18 | O(2^{N }* N) | Recursion, backtracking, and bit manipulation |
18-22 | O(2^{N }* N) | Recursion, backtracking, and bit manipulation |
30-40 | O(2^{N/2 }* N) | |
100 | O(N^{4}) | |
400 | O(N^{3}) | Dynamic programming, Constructive |
2K | O(N^{2}* log N) | Dynamic programming, Binary Search, Sorting, |
10K | O(N^{2}) | |
1M | O(N* log N) | Sorting, Binary Search, Divide and Conquer |
100M | O(N), O(log N), O(1) | Constructive, Mathematical, Greedy Algorithms |
Space Complexity: The space complexity of an algorithm quantifies the amount of space taken by an algorithm to run as a function of the length of the input. Consider an example: Suppose a problem to find the frequency of array elements.
The pseudo-code is as follows:
int freq[n]; int a[n]; for(int i = 0; i<n; i++) { cin>>a[i]; freq[a[i]]++; }
Below is the implementation of the above approach:
C++
// C++ program for the above approach #include <bits/stdc++.h> using namespace std; // Function to count frequencies of array items void countFreq( int arr[], int n) { unordered_map< int , int > freq; // Traverse through array elements and // count frequencies for ( int i = 0; i < n; i++) freq[arr[i]]++; // Traverse through map and print frequencies for ( auto x : freq) cout << x.first << " " << x.second << endl; } // Driver Code int main() { // Given array int arr[] = { 10, 20, 20, 10, 10, 20, 5, 20 }; int n = sizeof (arr) / sizeof (arr[0]); // Function Call countFreq(arr, n); return 0; } |
5 1 10 3 20 4
Here two arrays of length N, and variable i are used in the algorithm so, the total space used is N * c + N * c + 1 * c = 2N * c + c, where c is a unit space taken. For many inputs, constant c is insignificant, and it can be said that the space complexity is O(N).
There is also auxiliary space, which is different from space complexity. The main difference is where space complexity quantifies the total space used by the algorithm, auxiliary space quantifies the extra space that is used in the algorithm apart from the given input. In the above example, the auxiliary space is the space used by the freq[] array because that is not part of the given input. So total auxiliary space is N * c + c which is O(N) only.
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