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What is the time complexity of the following code function () for i 1?
Let W(n) and A(n) denote respectively, the worst case and average case running time of an algorithm executed on an input of size n….
| Code | Time complexity |
|---|---|
| for (i=1; I <= n; i*=2) | O(logn) because I is incremented exponentially and loop will run for less number of times than n. |
How many times the loop for i 1 i ni i * 2 will execute?
so it will go for infinite loop. Outer loop works O(log(n)) times and inner loop works O(n) times. So,time complexity will be O(nlogn) base 2.
How do you write time complexity of a code?
For example following functions have O(n) time complexity. For example Selection sort and Insertion Sort have O(n^2) time complexity. O(Logn) Time Complexity of a loop is considered as O(Logn) if the loop variables is divided / multiplied by a constant amount. For example Binary Search has O(Logn) time complexity.
What is quadratic time complexity?
Quadratic Time Complexity represents an algorithm whose performance is directly proportional to the squared size of the input data set (think of Linear, but squared). Within our programs, this time complexity will occur whenever we nest over multiple iterations within the data sets.
What is Nlogn time complexity?
O(nlogn) is known as loglinear complexity. O(nlogn) implies that logn operations will occur n times. O(nlogn) time is common in recursive sorting algorithms, sorting algorithms using a binary tree sort and most other types of sorts. The above quicksort algorithm runs in O(nlogn) time despite using O(logn) space.
What is time complexity and space complexity?
Time complexity is a function describing the amount of time an algorithm takes in terms of the amount of input to the algorithm. Space complexity is a function describing the amount of memory (space) an algorithm takes in terms of the amount of input to the algorithm.