A person claims that they can improve InsertionSort by the following argument. In the innermost loop of InsertionSort, instead of looping over all entries in th
given: public void trash(int n){ int i =n; while (i > 0){ for (int j = 0; j < n; j++) { System.out.println("*"); i
If the following loop structure is under Analysis of Upper bound, does it still compute to O(n^2)? I'm confused since inner loop has a dependency on the outer l
I'm not very familiar with Big O notation. I'm wondering does the time complexity of the nested loop always be n^2? For example the following function, the inne
I want to verify my assumptions about Time and Space complexity of two different implementations of valid palindrome functions in JavaScript. In the first imple
I am trying to make a function to measure the execution time of Big O algorithms. I have made a list with the names of the functions, and a list of n values, wh
I was working on a problem where you have to square the numbers in a sorted array on leetcode. Here is the original problem Given an array of integers A sor
When i was learning about Big O Notations , while getting to know the binary search algorithm as it requires sorting the array before searching . I had a questi
strings.Contains(str, substr) N = len(str) M = len(substr) Is Average case = O(N/2 + M) Worst case = O(N - M)?
I'm trying to find the time complexity of while loops and I have no idea where to begin. I understand how to find the complexity class of for loops, but when it
Prove that 1 + 1/2 + 1/3 + ... + 1/n is O(log n). Assume n = 2^k I put the series into the summation, but I have no idea how to tackle this problem. Any he
I was doing an example from Cracking the Coding Interview and I read that executing System.out.println(prefix); (where prefix is a String) would take "O(n) time
Big Omega is supposed to be the opposite of Big O, but they can always have the same value, because by definition Big O means: g(x) so that cg(x) is bigger or
for(int i = 0; i < n; i++) { for(int j = 0; j < i; j++){ // do swap stuff, constant time } } I read that single for loop is O(N) and trav
for( int bound = 1; bound <= n; bound *= 2 ) { for( int i = 0; i < bound; i++ ) { for( int j = 0; j < n; j += 2 ) { ... // c
Mega Sena is Brazil's most famous lottery. The number set ranges from 1 to 60 and a single bet can contain each from 6 to 15 numbers selected (the more numbers
What is O(log(n!)) and O(n!)? I believe it is O(n log(n)) and O(n^n)? Why? I think it has to do with Stirling Approximation, but I don't get the explanation v
I am curious. What is the correct way to describe this using Big-O Notation? var prices = [100, 180, 260, 590, 40, 310, 535, 10, 5, 3]; var biggest_profit = 0;
Trying to merge 3 arrays into one so that the final array is in order. Given int[] a = {1,3}; int[] b = {2,4}; int[] c = {1,5}; Merge the arrays so that t
It seems to be common knowledge that hash tables can achieve O(1), but that has never made sense to me. Can someone please explain it? Here are two situations