In this lesson, we will try to check if the given number is a power of 2. We solve this by writing an efficient algorithm that takes an optimal amount of time.
Let’s do another challenging question to check your understanding of Bitwise operators.
Example 01:
Input: 4
Output: True (As 4 is 2^2)
Example 02:
Input: 12
Output: False
Write a program to check if a given number is a power of 2 or not.
Let’s consider a number and find how the AND operator does this.
Input = 64
Output: True
We solve this by making use of the &
operator in computers. There are many ways to solve this, of which two approaches are simple, and one of them is a more complex but better solution.
Assume
n
is non-negative. We use the&
operator to achieve this.
Hint: The exciting part of calculating the power of 2 is that they have a set-bit count equal to one.
2
.
n
is not equal to 1
and if it is odd
, we will return false
.true
.Here is what our algorithm will look like:
export const IsEven = (n: number): boolean => {
function helper(value: number): boolean {
if (value === 0) {
return false;
}
while (value !== 1) {
if (value % 2 !== 0) {
return false;
}
value >>= 1;
}
return true;
}
return helper(n);
}
console.log(IsEven(6));
console.log(IsEven(8));
Time complexity: O(logn)
This takes log(n)
complexity. We can do better in constant time using the Brian Kernighan’s algorithm.
Space complexity: O(1)
The space complexity is O(1)
. It allocated no additional space.
First, inspect the code snippets above and think of a solution. This problem is designed for your practice, so try to solve it on your own first. If you get stuck, you can always refer to the solution provided in the solution section. Good luck!
export const isPow2 = (n: number): boolean => {
// Write - Your - Code- Here
return false; // change this, return true/false based on inputs
}
If you've got the answer, great! If not, it's normal, practice similar problems and you'll get a good hold of bit manipulation tricks.
I will explain the solution below.
Let's see how we make use of Brian Kernighan's algorithm to achieve this.
This is considered faster than the previous naive approach.
In this approach, we count the set bits. If a number is the power of 2, we know that only one set bit is present in its binary representation.
In binary, we go from right to left with powers of 2.
For example:
2^0, 2^1, 2^2, 2^3, 2^4, and so on.
Before we talk about algorithmic steps, review the tabular form of steps that depict the algorithm.
If (n & (n - 1) == 0)
, return True
.
else, False
.
Let’s visualize the values in the table below:
Let’s see a couple of examples:
n = 4 => 00000000 00000000 00000000 00000100
n - 1 = 3 => 00000000 00000000 00000000 00000011
-----------------------------------------------------------
(n & (n - 1)) = 0 => 00000000 00000000 00000000 00000000
-----------------------------------------------------------
(n&(n - 1))
, here this becomes 0
, which is true
. Hence, the number 4
is a power of 2.
n = 6 => 00000000 00000000 00000000 00000110
n - 1 = 5 => 00000000 00000000 00000000 00000101
-----------------------------------------------------------
(n & (n - 1)) = 4 => 00000000 00000000 00000000 00000100
-----------------------------------------------------------
(n&(n - 1))
is 4
, which is not equal to 0
. Hence, the number 6
is not a power of 2.
Let us look at the optimized approach.
Here is the reasoning behind this solution.
export const IsEven = (n: number): boolean => {
function helper(value: number): boolean {
if (value === 0) {
return false;
}
return (value & (value - 1)) === 0;
}
return helper(n);
}
console.log(IsEven(6));
console.log(IsEven(8));
We can further simplify this code into a single line shown below.
const IsEven = (n: number): boolean => {
return n !== 0 && (n & (n - 1)) === 0;
}
console.log (IsEven (6));
console.log (IsEven (8));
Time complexity: O(1)
The run time depends on the number of 1-bits
in n
. In the worst case, all bits in n
are 1-bits
. With a 32-bit
integer, the runtime is O(1)
.
Space complexity: O(1)
The space complexity is O(1)
. It allocated no additional space.
If you are interested in mastering bit tricks, I've got a course that more than 100k+ programmers loved.
In this course, you will learn how to solve problems using bit manipulation, a powerful technique that can optimize your algorithmic and problem-solving skills. The course has a simple explanation with sketches, detailed step-by-step drawings, and various ways to solve it using bitwise operators.
These bit-tricks could help in competitive programming and coding interviews in running algorithms, mostly in O(1)
time.
This is one of the most important/critical topics when someone prepares for coding interviews for FAANG(Facebook, Amazon, Apple, Netflix, and Google) companies.
To kick things off, you’ll start by learning about the number system and how it’s represented. Then you’ll move on to learn about the six different bitwise operators: AND, OR, NOT, XOR, and bit shifting. Throughout, you will get tons of hands-on experience working through practice problems to help sharpen your understanding.
By the time you’ve completed this course, you will solve problems faster with greater efficiency!! 🤩
Link to my course: Master Bit Manipulation for Coding Interviews.
Co-published here.