Asymptotic Time complexity, Running Time analysis of Algorithms-Asymptotic Time complexity-Part 2-GATE exam preparation videos for computer science, asymptotic analysis of algorithms, algorithm efficiency analysis, how to compute the running time of an algorithm, complexity of algorithm, finding running time of an algorithm, notes on running time of algorithms, analysing algorithm, growth rate algorithms find maximum element in array Time complexity Analysis of iterative programs Time complexity of a triple-nested loop time complexity of algorithms for loop time complexity examples time complexity of while loop for (int i = 1; i less than equal to n; ++i) for (int j = i; j less than equal to n; ++j) for (int k = j; k less than equal to n; ++k) // statement The statement here is executed exactly n(n+1)(n+2)6 times. 1) O(1): Time complexity of a function (or set of statements) is considered as O(1) if it doesn’t contain loop, 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 less than equal to 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 less than equal to n; i += c) { // some O(1) expressions } for (int i = n; i Greater Than 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 less than equal to n; i += c) { for (int j = 1; j less than equal to n; j += c) { // some O(1) expressions } } for (int i = n; i Greater Than 0; i += c) { for (int j = i+1; j less than equal to 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 less than equal to n; i *= c) { // some O(1) expressions } for (int i = n; i Greater Than 0; i /= c) { // some O(1) expressions } For example Binary Search(refer iterative implementation) has O(Logn) time complexity. 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 less than equal to 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 Greater Than 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 less than equal to m; i += c) { // some O(1) expressions } for (int i = 1; i less than equal to 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. analysis of algorithms and computational complexity time and space complexity of algorithms time complexity calculation examples notions of space and time complexity algorithm analysis examples complexity of algorithm pdf analyzing algorithm big 0 notation

• published: 05 Apr 2015
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