Flowchart - How to Solve Coding Problem?

About

This section explains the stepwise thought process interviewers expect: understanding the problem, clarifying constraints, writing brute force first, then optimizing. Questions help reinforce this structure with examples.

Reference - https://www.crackingthecodinginterview.com/

Example 1

Problem

Given an array of distinct integer values, count the number of pairs of integers that have difference k. For example, given the array { 1, 7, 5, 9, 2, 12, 3} and the difference k = 2, there are four pairs with difference as 2 i.e. (1, 3), (3, 5), (5, 7), (7, 9).

Step 1: Understand the Problem

We need to count distinct pairs (a, b) in an array such that:

∣a−b∣=k

Given:

• An array of distinct integers.

• An integer k (positive difference).

• We count unordered pairs (e.g., (1,3) is the same as (3,1)).

Step 2: Explore Examples

Example:

Input:  { 1, 7, 5, 9, 2, 12, 3 }, k = 2
Output: 4

Valid pairs: (1,3), (3,5), (5,7), (7,9)

Edge cases:

• Empty array? → Return 0

• Single element? → Return 0

• No valid pairs? → Return 0

• Negative k? → Not allowed (problem states "difference k")

• Already sorted? → Optimize accordingly

Step 3: Brute Force Approach

A naïve solution checks every pair (i, j):

for (int i = 0; i < arr.length; i++) {
    for (int j = i + 1; j < arr.length; j++) {
        if (Math.abs(arr[i] - arr[j]) == k) {
            count++;
        }
    }
}

Time Complexity:

• O(N²) (Nested loop) – Too slow for large inputs.

Step 4: Optimize

We can use a HashSet to check for pairs in O(N) time.

Optimized Approach:

1. Insert all numbers into a HashSet (O(N) time)

2. For each number x, check if (x + k) exists in the set

   • (x, x+k) is a valid pair

   • No need to check both (x, x-k) since it’s already handled by previous elements

Time Complexity: O(N) Space Complexity: O(N)

Step 5: Write Clean Code

import java.util.HashSet;

public class PairDifference {
    public static int countPairsWithDifference(int[] arr, int k) {
        if (arr == null || arr.length == 0 || k < 0) return 0;

        HashSet<Integer> set = new HashSet<>();
        int count = 0;

        // Store all numbers in HashSet
        for (int num : arr) {
            set.add(num);
        }

        // Check for (num + k) in HashSet
        for (int num : arr) {
            if (set.contains(num + k)) {
                count++;
            }
        }

        return count;
    }

    public static void main(String[] args) {
        int[] arr = {1, 7, 5, 9, 2, 12, 3};
        int k = 2;
        System.out.println(countPairsWithDifference(arr, k)); // Output: 4
    }
}

Step 6: Test the Solution

✔ Base case: [] → 0 ✔ Single element: [5] → 0 ✔ No valid pairs: [10, 20, 30] with k = 5 → 0 ✔ Large input: Handles efficiently in O(N)

Example 2

Problem

Print all positive integer solutions to the equation a3 + b3 and d are integers between 1 and 1000.

Understanding the Problem

We need to find all positive integer solutions for the equation:

a^3+b^3=c^3+d^3

where a,b,c,d are integers between 1 and 1000.

This means we need to find pairs (a, b) and (c, d) such that their cubes sum to the same value.

Brute Force Approach (O(N⁴))

A simple approach is to iterate through all possible values of (a, b, c, d) and check if they satisfy the equation.

for (int a = 1; a <= 1000; a++) {
    for (int b = 1; b <= 1000; b++) {
        for (int c = 1; c <= 1000; c++) {
            for (int d = 1; d <= 1000; d++) {
                if (Math.pow(a, 3) + Math.pow(b, 3) == Math.pow(c, 3) + Math.pow(d, 3)) {
                    System.out.println(a + "^3 + " + b + "^3 = " + c + "^3 + " + d + "^3");
                }
            }
        }
    }
}

Correct but VERY slow: O(N⁴) ≈ 10¹² iterations, which is impractical.

Optimized Approach using HashMap (O(N²))

1. Instead of iterating over four nested loops, we can use a HashMap to store sums of cube pairs.

2. We iterate over (a, b) pairs first, compute a^3 + b^3, and store it in a HashMap.

3. Then, for each (c, d) pair, we check if the sum exists in the HashMap.

Time Complexity:

• O(N²) for precomputing all (a, b) pairs.

• O(N²) for checking (c, d) pairs.

• Total: O(N²) ≈ 10⁶ iterations, which is feasible.

Optimized Java Implementation

import java.util.*;

public class CubePairEquation {
    public static void findCubePairs(int limit) {
        // HashMap to store sum of cubes and corresponding (a, b) pairs
        Map<Integer, List<int[]>> cubeSumMap = new HashMap<>();

        // Step 1: Compute all pairs (a, b) and store in HashMap
        for (int a = 1; a <= limit; a++) {
            for (int b = a; b <= limit; b++) { // Avoid duplicate pairs
                int sum = (a * a * a) + (b * b * b);
                
                // Store pairs with the same sum
                cubeSumMap.computeIfAbsent(sum, k -> new ArrayList<>())
                    .add(new int[]{a, b});
            }
        }

        // Step 2: Iterate over the map and print valid (a, b) and (c, d) pairs
        for (Map.Entry<Integer, List<int[]>> entry : cubeSumMap.entrySet()) {
            List<int[]> pairs = entry.getValue();
            for (int i = 0; i < pairs.size(); i++) {
                for (int j = i + 1; j < pairs.size(); j++) {
                    int[] firstPair = pairs.get(i);
                    int[] secondPair = pairs.get(j);
                    System.out.println(firstPair[0] + "^3 + " + firstPair[1] + "^3 = "
                            + secondPair[0] + "^3 + " + secondPair[1] + "^3");
                }
            }
        }
    }

    public static void main(String[] args) {
        findCubePairs(1000); // Find all solutions for numbers from 1 to 1000
    }
}

Explanation of Optimized Approach

1. Precompute cube sums:

   • We iterate over (a, b) pairs and compute a^3 + b^3.

   • Store these values in a HashMap, where key = sum of cubes, and value = list of (a, b) pairs.

2. Find matching pairs efficiently:

   • If multiple (a, b) pairs have the same cube sum, they form valid (c, d) pairs.

   • This eliminates two nested loops, making the solution O(N²).

Time & Space Complexity

Time Complexity: O(N²) (Better than O(N⁴)) Space Complexity: O(N²) (Stores sum mappings)

Example Output (Partial)

1^3 + 12^3 = 9^3 + 10^3
2^3 + 16^3 = 9^3 + 15^3
...