Big O Notation Practice Problems Online

Prefix sums of a sequence of numbers $a_0,a_1,a_2,a_3\ldots$ are given by a second sequence of numbers $s_0,s_1,s_2,s_3\ldots$ defined as

$s_j = \sum_{i=1}^{j}a_i$

The Python code below computes prefix sums.

def s(a, j):
    prefix_sum = 0
    while j >= 0:
        i = 0
        while i <= j:
            prefix_sum += a[i]
            i += 1
        j -= 1
        return prefix_sum

Analyze the the code and find the line or lines that will be executed the greatest number of times.

The fragment of Java code below describes a recursive method $P$ that takes two integer parameters $x$ and $n$ and returns an integer.

public int P(int x , int n){
    if (n == 0){
        return 1;
    }
    if (n % 2 == 1){
        int y = P(x, (n - 1) / 2);
        return x * y * y;
    }
    else{
        int y = P(x, n / 2);
        return y * y;               
    }
}

Analysis shows us that the running time of this function for large $n$ can be expressed as $T(n) = O(g(n))$ for some function $g(n)$ . What is a valid function $g(n)$ ?

Details and assumptions

$g(n)$ accurately describes the asymptotic runtime of $P(x,y)$ .
Disregard the limitations of the int primitive type and assume the behavior of the method is not affected for large inputs.

int function(int n)
{
   int i;

   if (n <= 0) {
       return 0;
   } else {
       i = random(n - 1);
       return function(i) + function(n - 1 - i);
   }
}

Consider the recursive algorithm above, where the random(int n) spends one unit of time to return a random integer which is evenly distributed within the range $[0,n]$ . If the average processing time is $T(n)$ , what is the value of $T(6)$ ?

Assume that all instructions other than the random cost a negligible amount of time.

The simple recursive python program below can be used to compute the $n^{th}$ Fibonnaci number.

def Fibonacci(n):
    if n in [0, 1]:
        return n
    else:
        return Fibonacci(n-1) + Fibonacci(n-2)

It can be shown that a tight bound of this program is given by $T(n) = \Theta(g(n))$ for some function $g(n)$ . What is the value of $g(17)$ ?

Details and assumptions

An obvious algorithm for exponentiation ( $x^{n}$ ) takes $n-1$ multiplications. Suppose we propose a faster algorithm for the case where $n=2^{m}$ . The "Big-O" complexity of such an algorithm can be expressed as:

$T(n)=O(g(n))$

Take the value of $g(n)$ for an arbitrary $n$ . If we multiply $n$ by some constant $k$ , how will the value of $g(n)$ most likely change?

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