Knowledge Guide
HomeDSARecursion

Solution Powx,n

Problem Statement

Write a Recursive Approach to Calculate Power of Integer to N Pow(x,n).

Given an integer x and an integer n, write a recursive function to calculate the power of x to the nth power.

Example

Sr#xnOutputDescription
125322 raised to the power of 5 equals 32.
234813 raised to the power of 4 equals 81.
3501Any number raised to the power of 0 equals 1 (x^0 = 1).

Constraints:

Solution

To calculate the power of x to the nth power recursively, we can follow these steps:

  1. If n is equal to 0, return 1 since any number raised to the power of 0 equals 1.
  2. If n is less than 0, return 1 divided by the result of calculating x raised to the power of -n.
  3. If n is greater than 0, recursively calculate x raised to the power of n/2. Store the result in a variable temp.
  4. If n is even, return the square of temp (i.e., temp * temp).
  5. If n is odd, return x multiplied by the square of temp (i.e., x * temp * temp).

This approach works by dividing the power n into smaller subproblems, reducing the number of recursive calls. By utilizing the property that x^(2k) is equal to (x^k)^2, we can optimize the computation and reduce the time complexity.

Image
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Code

Here is the code for this algorithm:

java
public class Solution {

  public static double calculatePower(double x, int n) {
    if (n == 0) {
      return 1;
    }
    if (n < 0) {
      return 1 / calculatePower(x, -n);
    }
    double temp = calculatePower(x, n / 2);
    if (n % 2 == 0) {
      return temp * temp;
    } else {
      return x * temp * temp;
    }
  }

  public static void main(String[] args) {
    // Example inputs
    double x1 = 2;
    int n1 = 5;
    double x2 = 3;
    int n2 = 4;
    double x3 = 5;
    int n3 = 0;

    // Calculate power and print results
    System.out.println(
      "Power of " +
      x1 +
      " to the power of " +
      n1 +
      " = " +
      calculatePower(x1, n1)
    );
    System.out.println(
      "Power of " +
      x2 +
      " to the power of " +
      n2 +
      " = " +
      calculatePower(x2, n2)
    );
    System.out.println(
      "Power of " +
      x3 +
      " to the power of " +
      n3 +
      " = " +
      calculatePower(x3, n3)
    );
  }
}

Time and Space Complexity

The time complexity of this algorithm is because we divide the problem size in half with each recursive call. The space complexity is as well because the maximum depth of the recursion is log n.

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