Solved "Isomorphic Strings" problem using Java and HashMap

Day 90 of #100DaysOfCode Solved Squares of a Sorted Array in Java 🔠 Approach The task was to take an array of integers sorted in non-decreasing order, square each number, and then return the result array also sorted in non-decreasing order. Brute-Force Method The solution implemented here is a straightforward two-step brute-force approach: Squaring: I iterated through the input array nums and replaced each element with its square (i.e., nums[i] * nums[i]). This handles both positive and negative numbers correctly. Sorting: After squaring all elements, I used Java's built-in Arrays.sort(nums) method to sort the entire array. While correct, this approach has a time complexity dominated by the sorting step, which is O(NlogN), where N is the number of elements. The runtime of 10 ms shows that a more efficient, two-pointer approach (which can solve this in O(N) time) is generally preferred for optimal performance. #Java #100DaysOfCode #LeetCode #CodingChallenge #Algorithms #Array #Sorting #ProblemSolving

#Day45 of #50DaysOfCoding Divisible Sum Pairs Problem (Java) Today, I tackled the “Divisible Sum Pairs” problem from HackerRank using Java. The objective was to identify all pairs of numbers in a list whose sum is divisible by a specified integer k. Concepts Used: - Nested loops to check every unique pair (i, j) where i < j. - Modulo operation to verify divisibility: (ar[i] + ar[j]) % k == 0. - Basic iteration and condition checking logic. Working: - Read input values n, k, and the array ar. - Iterate through all pairs. - Count how many pairs have sums divisible by k. - Print the total count. Example: If n = 6, k = 3, and ar = [1, 3, 2, 6, 1, 2], the valid pairs are (1,2), (3,6), (2,4), etc. Output: 5 Complexity: - Time Complexity: O(n²) - Space Complexity: O(1) This exercise enhanced my understanding of modulo operations, loops, and pair iteration logic — essential concepts for many coding interviews. #Java #ProblemSolving #CodingJourney #Programming #LearningEveryday #LogicBuilding #leetcode #DSA #CodingChallenge #LearnJava #CodeNewbie #Algorithms #DataStructures #TechJourney #javaProgramming #LearningInPublic #PentagonSpace

Day 80 of #100DaysOfCode Solved Frequency Sort in Java 🔠 Approach Today's problem was to sort an array based on the frequency of its numbers. My initial solution was accepted, but it exposed a major efficiency gap! My strategy involved: Sorting the array first. Using nested loops to count the frequency of each number and store it in a HashMap. (Implied) Using the counts to perform the final custom sort. The core issue was the counting method: running a full loop inside another full loop to get the frequency. This quadratic $O(N^2)$ counting completely tanked the performance. ✅ Runtime: 29 ms (Beats 12.02%) ✅ Memory: 45.26 MB (Beats 5.86%) Another great lesson in algorithmic complexity! The difference between $O(N^2)$ and the optimal $O(N \log N)$ for this problem is massive. Time to refactor and implement the fast, single-pass $O(N)$ frequency count using getOrDefault! 💪 #Java #100DaysOfCode #LeetCode #CodingChallenge #Algorithms #Arrays #HashMap #Optimization #ProblemSolving

Day 19 of #100DaysOfLeetCode 💻 Today’s challenge was about finding two unique numbers in an array where every other number appears twice. At first, I tried using an ArrayList — adding numbers if they weren’t present and removing them if they already existed. The logic worked, but I ran into type conversion issues (Object cannot be converted to int). That’s when I learned the importance of using generics in Java (ArrayList<Integer> instead of raw ArrayList). Small syntax details, but they make all the difference! 🔍 Key takeaways: Always specify the data type in collections. Understand how remove() behaves differently for index vs value. Even a brute-force approach can teach valuable debugging lessons. #Day19 #LeetCode #Java #CodingJourney #100DaysOfCode #Debugging #ProblemSolving

✨ Day 74 of #100DaysOfCode Today, I solved LeetCode 22. Generate Parentheses using Backtracking in Java 🧠💻 📝 Problem Summary: Given n pairs of parentheses, generate all combinations of well-formed parentheses. ✅ Example: Input: n = 3 Output: ["((()))","(()())","(())()","()(())","()()()"] 💡 Key Idea: Use backtracking to build all valid strings. Keep track of the number of opening and closing brackets. Add '(' if open < n. Add ')' if close < open. When the string reaches length 2 * n, it's a valid combination. 🚀 Learning: Backtracking is a powerful technique for exploring all valid combinations. Keeping proper state (open, close) helps prune invalid paths early. #100DaysOfCode #LeetCode #DSA #Java #ProblemSolving #Backtracking #CodingChallenge #SowmiyaCodes #PlacementPrep

Day 28/100 – #100DaysOfCode 🚀 | #Java #LeetCode #DynamicProgramming ✅ Problem Solved: Scramble String 🔀 🧩 Problem Summary: Given two strings s1 and s2, determine whether one is a scrambled version of the other. A string is scrambled by recursively dividing it into two non-empty substrings and swapping them. 💡 Approach Used: Implemented Recursion + Memoization using a HashMap for overlapping subproblems. Used character frequency checks to prune unnecessary recursion calls. Used Java’s BiFunction with inline helper logic for recursion. ⚙️ Time Complexity: O(n⁴) (due to substring operations and recursion) 📦 Space Complexity: O(n²) ✨ Takeaway: Even complex recursive problems can be optimized efficiently with Memoization and early pruning. 🚀 #Java #LeetCode #DynamicProgramming #Recursion #ProblemSolving #100DaysOfCode #CodingChallenge

💡 Java Collections — Under the Hood Understanding how collections work = writing faster, efficient code. 🔹 HashMap → Key-value, hash buckets + tree structure (since Java 8) 🔹 HashSet → Built on HashMap 🔹 ArrayList → Dynamic resizing arrays 🔹 LinkedList → Doubly linked nodes 🔹 TreeMap → Red-black tree sorted by key Master these, and performance tuning becomes instinctive ⚙️ 💬 Which one do you use most often — and why? #Java #DataStructures #Coding #SystemDesign #BackendDevelopment

Day 89 of #100DaysOfCode Solved Find Numbers with Even Number of Digits in Java 🔢 Approach The goal of this problem was to count how many integers in a given array have an even number of digits. I implemented two methods: findNumbers(int[] nums): This is the main function that iterates through every number in the input array nums. isEvenOrOdd(int n): This helper function takes an integer and determines if its digit count is even or odd. Inside the helper function, I used a while loop to count the digits: I initialized a count to 0. The loop continues as long as $n > 0$. In each iteration, I increment count and then perform integer division by 10 (n = n / 10) to remove the least significant digit. Finally, I return true if the total count of digits is even (count \% 2 == 0). This efficient, digit-by-digit checking approach resulted in a strong performance, beating 98.90% of other submissions. #Java #100DaysOfCode #LeetCode #CodingChallenge #Algorithms #Array #ProblemSolving #Optimization

Day 85 of #100DaysOfCode Solved Range Sum Query - Immutable (NumArray) in Java ➕ Approach Today's problem was a classic that demonstrates the power of pre-computation: finding the sum of a range in an array many times. The optimal solution is the Prefix Sum technique. Pre-computation: In the constructor, I built a prefixSum array where prefixSum[i] holds the sum of all elements from index 0 up to index $i-1$ in the original array. This takes $O(N)$ time. $O(1)$ Query: The magic happens in the sumRange(left, right) method. The sum of any range $[left, right]$ is found instantly by calculating prefixSum[right + 1] - prefixSum[left]. The cost of a single $O(N)$ setup is outweighed by the ability to perform every subsequent query in $O(1)$ time! #Java #100DaysOfCode #LeetCode #CodingChallenge #Algorithms #Arrays #PrefixSum #Optimization #ProblemSolving

🚀 Day 55 of #100DaysOfCode Challenge Problem: LeetCode #219 – Contains Duplicate II Language: Java ☕ Today I solved an interesting array problem that checks whether a duplicate element exists within a given distance k in the array. 💡 Logic: Use a HashMap to remember the last index of each number. If the same number appears again, check if the difference between indices ≤ k. If yes → return true, else keep checking. 💻 Code: import java.util.HashMap; public class Solution { public boolean containsNearbyDuplicate(int[] nums, int k) { HashMap<Integer, Integer> map = new HashMap<>(); for (int i = 0; i < nums.length; i++) { if (map.containsKey(nums[i]) && i - map.get(nums[i]) <= k) { return true; } map.put(nums[i], i); } return false; } } 🧠 Example: Input: [1,2,3,1], k = 3 Output: true ✅ (same number 1 appears within distance 3) ⚙️ Key Concepts: HashMap for quick lookup Difference check using indices Time Complexity → O(n) Space Complexity → O(n) 💬 Every day’s problem teaches me something new — today it was about using maps smartly to track elements efficiently. On to the next challenge! 💪 #Day55 #LeetCode #Java #100DaysOfCode #CodingJourney #ProblemSolving #HashMap #DSA