Analysis of Algorithm

Analysis of Loops

Analysis of Loops
Analysis of Loops - Analysis of Algorithm - O(1): Time complexity of a function (or set of statements) is considered as O(1) if it doesn’t contain Analysis.

1) O(1): Time complexity of a function (or set of statements) is considered as O(1) if it doesn’t contain Analysis of Loops, recursion and call to any other non-constant time function.

// set of non-recursive and non-loop statements

For example swap() function has O(1) time complexity.

A loop or recursion that runs a constant number of times is also considered as O(1). For example the following loop is O(1).

 // Here c is a constant 
 for (int i = 1; i <= c; i++) { 
 // some O(1) expressions
 }

2) O(n): Time Complexity of a loop is considered as O(n) if the loop variables is incremented / decremented by a constant amount. For example following functions have O(n) time complexity.

// Here c is a positive integer constant 
 for (int i = 1; i <= n; i += c) { 
 // some O(1) expressions
 }

for (int i = n; i > 0; i -= c) {
 // some O(1) expressions
 }

3) O(nc): Time complexity of nested loops is equal to the number of times the innermost statement is executed. For example the following sample loops have O(n2) time complexity

 for (int i = 1; i <=n; i += c) {
 for (int j = 1; j <=n; j += c) {
 // some O(1) expressions
 }
 }

for (int i = n; i > 0; i += c) {
 for (int j = i+1; j <=n; j += c) {
 // some O(1) expressions
 }

For example Selection sort and Insertion Sort have O(n2) time complexity.

4) O(Logn) :Time Complexity of a loop is considered as O(Logn) if the loop variables is divided/ multiplied by a constant amount.

 for (int i = 1; i <=n; i *= c) {
 // some O(1) expressions
 }
 for (int i = n; i > 0; i /= c) {
 // some O(1) expressions
 }

For example Binary Search(refer iterative implementation) has O(Logn) time complexity.

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5) O(LogLogn) :Time Complexity of a loop is considered as O(LogLogn) if the loop variables is reduced / increased exponentially by a constant amount.

// Here c is a constant greater than 1 
 for (int i = 2; i <=n; i = pow(i, c)) { 
 // some O(1) expressions
 }
 //Here fun is sqrt or cuberoot or any other constant root
 for (int i = n; i > 0; i = fun(i)) { 
 // some O(1) expressions
 }

How to combine time complexities of consecutive loops?

When there are consecutive loops, we calculate time complexity as sum of time complexities of individual loops.

for (int i = 1; i <=m; i += c) { 
 // some O(1) expressions
 }
 for (int i = 1; i <=n; i += c) {
 // some O(1) expressions
 }
 Time complexity of above code is O(m) + O(n) which is O(m+n)
 If m == n, the time complexity becomes O(2n) which is O(n).

How to calculate time complexity when there are many if, else statements inside loops?

As discussed here, worst case time complexity is the most useful among best, average and worst. Therefore we need to consider worst case. We evaluate the situation when values in if-else conditions cause maximum number of statements to be executed.

For example consider the linear search function where we consider the case when element is present at the end or not present at all.

When the code is too complex to consider all if-else cases, we can get an upper bound by ignoring if else and other complex control statements.

How to calculate time complexity of recursive functions?

Time complexity of a recursive function can be written as a mathematical recurrence relation. To calculate time complexity, we must know how to solve recurrences. We will soon be discussing recurrence solving techniques as a separate post.

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About the author

Venkatesan Prabu

Venkatesan Prabu

Wikitechy Founder, Author, International Speaker, and Job Consultant. My role as the CEO of Wikitechy, I help businesses build their next generation digital platforms and help with their product innovation and growth strategy. I'm a frequent speaker at tech conferences and events.

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