to be able to pop i elements out of a stack we must first check if the stack actually has i or more elements and return false if it doesnt. If it does, we will count i elements and make top point to the i+1 th element from the top of the stack
bool multipop(i, node* top)
{
node * temp = ⊤ int count = 1;
while(count<i)
{
if(temp->next == NULL)
return false;
else
{
temp = temp->next; count ++;
}
}
top = &temp;
return true;
}
this process takes O(i) time.
(a) Prove that n log^3 n is O(n^2). Prove that n^3 is not O(n^2 log n)....
1. Prove that log2(n) is O(n) 2. Prove that log(n!) is O(n log(n))
Using a recurrence relation, prove that the time complexity of the binary search is O(log n). You can use ^ operator to represent exponentiation operation. For example, 2^n represents 2 raised to the power of n.
What is the computational complexity of deleting an item from a heap? O(1) O(log n) O(n) O(n log n) O(n^2) (n squared)
poin (a) 20n-O(n) (c) n=o(log n) (e) log n!= 0(n log nioo) (b) 3(2) 2: 100
Prove this using the definition
R7: log(n*) is O(log n) for any fixed x > 0
1) Which of the following are in O (n): a) n + lg n b) n + 2n c) n + n2 d) 1000 n + 4500 lg n + 54 n 2) What is a stack? Give an ADT. 3) How would you implement the push method for a Stack implemented as a Linked List? 4) How would you implement the pop method for a Stack implemented as a Linked List?
1. For each of the following, prove using the definition of O): (a) 7n + log(n) = O(n) (b) n2 + 4n + 7 =0(na) (c) n! = ((n") (d) 21 = 0(221)
O(log(log(N))) < O(log(N)) a. True b. False O(N ) < O(log(N)) a. True b. False O( N5) < O(N2 - 3N + 2) a. True b. False O(2N) < O(N2) a. True b. False
n)2" log log(n)O(n)? I don't How does =n. VIn) T n understand how VITn) 2" log 7 -)? I know we can take out the T, because 1) Vn) T* n it's in our natural logarithm. It's a constant factor. but how does (n) show up in the denominator after it used to be in the numerator? I need to know how the expression (1) right on the left is equal to the expression (1) on the n)2" log log(n)O(n)?...
What is the time complexity of this code?
I'm unsure if it is O(log(n)) or O(n).
I think that the while loop is logn but the for loop that comes
after runs the same number of times as the while loop.
string toBinary(int num) { string binary = "", temp = ""; while (num != 0) { temp += to_string(num%2); num /= 2; for (int i = temp.size() - 1; i >= 0; i--) { binary += temp[i]; return binary;