Reconstructing Digits from English Names with Java

🚀 Day 97 of My 100 Days LeetCode Challenge | Java Today’s problem was a solid exercise in matrix processing and prefix sum optimization. The goal was to count the number of submatrices whose sum is less than or equal to a given value (k). A brute-force approach would be too slow, so the key was to use 2D prefix sums to efficiently compute submatrix sums. By converting the matrix into a prefix sum matrix, we can calculate the sum of any submatrix in constant time, making the overall solution much more efficient. ✅ Problem Solved: Count Submatrices With Sum ≤ K ✔️ All test cases passed (859/859) ⏱️ Runtime: 7 ms 🧠 Approach: 2D Prefix Sum 🧩 Key Learnings: ● Prefix sums are powerful for optimizing repeated range sum queries. ● 2D prefix sums extend the same idea from arrays to matrices. ● Preprocessing can drastically reduce computation time. ● Avoiding brute force is key in large input problems. ● Matrix problems often become easier with the right transformation. This problem reinforced how preprocessing techniques like prefix sums can turn complex problems into efficient solutions. 🔥 Day 97 complete — sharpening matrix optimization and prefix sum skills. #LeetCode #100DaysOfCode #Java #PrefixSum #Matrix #Algorithms #ProblemSolving #DSA #CodingJourney #Consistency

🚀 Day 93 of My 100 Days LeetCode Challenge | Java Today’s problem explored recursion and backtracking, focusing on generating valid strings under specific constraints. The challenge was to generate happy strings of length n, where: The string consists only of 'a', 'b', and 'c' No two adjacent characters are the same From all possible happy strings, the goal was to find the k-th lexicographical string. To solve this, I used a backtracking approach that builds the string step by step. At each step, we try adding characters while ensuring they don't match the previous character. This naturally generates all valid happy strings in lexicographical order. Once all valid strings are generated, we simply return the k-th one if it exists. ✅ Problem Solved: The k-th Lexicographical Happy String of Length n ✔️ All test cases passed (345/345) ⏱️ Runtime: 22 ms 🧠 Approach: Backtracking + Recursion 🧩 Key Learnings: ● Backtracking is ideal for generating constrained combinations. ● Pruning invalid states early improves efficiency. ● Recursion makes exploring possible paths intuitive. ● Lexicographical generation often comes naturally with ordered iteration. ● Constraint-based string generation is a common interview pattern. This problem was a great exercise in systematically exploring possibilities while enforcing constraints. 🔥 Day 93 complete — strengthening recursion and backtracking intuition. #LeetCode #100DaysOfCode #Java #Backtracking #Recursion #ProblemSolving #DSA #CodingJourney #Consistency

🚀 Day 96 of My 100 Days LeetCode Challenge | Java Today’s problem was a great combination of matrix manipulation and optimization thinking. The challenge was to find the largest submatrix consisting of only 1’s, where we are allowed to rearrange the columns in each row. At first, it looks like a standard matrix problem, but the twist is the column rearrangement, which changes how we approach it. Instead of checking fixed columns, we treat each row like a histogram and sort column heights to maximize the possible area. By building heights of consecutive 1’s and then sorting each row, we can efficiently compute the maximum possible rectangle area. ✅ Problem Solved: Largest Submatrix With Rearrangements ✔️ All test cases passed (59/59) ⏱️ Runtime: 13 ms 🧠 Approach: Matrix + Greedy + Sorting 🧩 Key Learnings: ● Transforming a matrix into histogram heights simplifies the problem. ● Sorting can help maximize outcomes when order is flexible. ● Greedy strategies work well when rearrangements are allowed. ● Small problem twists (like column swaps) can completely change the approach. ● Thinking in terms of heights and widths helps in area-based problems. This problem was a reminder that changing perspective (matrix → histogram) can make complex problems much simpler. 🔥 Day 96 complete — improving matrix problem-solving and optimization skills. #LeetCode #100DaysOfCode #Java #Matrix #Greedy #Algorithms #ProblemSolving #DSA #CodingJourney #Consistency

🚀 LeetCode Day 18 – Complement of Base 10 Integer Today I solved LeetCode Problem 1009: Complement of Base 10 Integer using Java. 🧠 Problem: Given a non-negative integer n, return the complement of its binary representation. The complement means flipping all bits in the binary form of the number. 📌 Example: Input: n = 5 Binary of 5 → 101 Complement → 010 Output → 2 💡 Approach: • Create a bitmask with all bits set to 1 up to the highest bit of n. • Use the XOR (^) operator with the mask to flip the bits. 💻 Java Code: class Solution { public int bitwiseComplement(int n) { if (n == 0) return 1; int mask = 0, temp = n; while (temp > 0) { mask = (mask << 1) | 1; temp >>= 1; } return n ^ mask; } } ⏱️ Time Complexity: O(log n) 📦 Space Complexity: O(1) 📚 Practicing Data Structures & Algorithms daily to improve problem-solving skills. #LeetCode #Java #DSA #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Day 99 of My 100 Days LeetCode Challenge | Java Today’s problem was all about randomization and linked list traversal — a nice break from heavy DP and matrices. The challenge was to design a system that returns a random node’s value from a singly linked list, ensuring that every node has an equal probability of being chosen. Since linked lists don’t allow direct indexing, the key idea was to first determine the size of the list, and then generate a random index to fetch the corresponding node. This approach ensures uniform randomness while keeping the implementation simple and efficient. ✅ Problem Solved: Linked List Random Node ✔️ All test cases passed (8/8) ⏱️ Runtime: 11 ms 🧠 Approach: Linked List Traversal + Randomization 🧩 Key Learnings: ● Randomization problems require ensuring uniform probability distribution. ● Linked lists limit direct access, so traversal becomes essential. ● Precomputing size can simplify random selection. ● Sometimes simple approaches are the most effective. ● Understanding data structure limitations helps design better solutions. This problem highlighted how probability + data structures can come together in elegant ways. 🔥 Day 99 complete — sharpening my understanding of randomization and linked list behavior. #LeetCode #100DaysOfCode #Java #LinkedList #Randomization #Algorithms #ProblemSolving #DSA #CodingJourney #Consistency

🚀Day 24 of #75DaysofLeetCode LeetCode Problem Solved: Removing Stars From a String (2390) Today I solved the problem “Removing Stars From a String.” 🔹 Problem Statement: We are given a string containing characters and *. Each * removes the closest non-star character to its left, along with the * itself. The task is to return the final string after all stars are processed. 💡 Approach: The most efficient way to solve this is using a Stack-like approach. 1️⃣ Traverse the string from left to right. 2️⃣ If the character is not *, add it to the result (stack). 3️⃣ If the character is *, remove the last added character. 4️⃣ Continue until the string is processed. ⏱ Time Complexity: O(n) 📦 Space Complexity: O(n) 💻 Java Implementation: class Solution { public String removeStars(String s) { StringBuilder sb = new StringBuilder(); for(char c : s.toCharArray()) { if(c == '*') { sb.deleteCharAt(sb.length() - 1); } else { sb.append(c); } } return sb.toString(); } } ✨ Key Learning: Using a stack or StringBuilder helps efficiently simulate the removal of the closest character on the left. #LeetCode #Java #DataStructures #ProblemSolving #CodingPractice #100DaysOfCode

🚀 Day 536 of #750DaysOfCode 🚀 Today I solved Count Submatrices With Equal Frequency of X and Y (LeetCode 3212) using Java. 🔹 Problem Summary: Given a grid containing 'X', 'Y', and '.', we need to count the number of submatrices starting from the top-left corner (0,0) such that: • The number of 'X' and 'Y' is equal • The submatrix contains at least one 'X' 🔹 Approach: Instead of checking every possible submatrix (which would be too slow), I used the Prefix Sum technique. I maintained two prefix matrices to store the count of 'X' and 'Y' up to each cell. For every position (i, j), I checked: countX == countY and countX > 0 If true, that submatrix is valid. This reduces the complexity to O(n × m), which works efficiently for large grids. 🔹 Key Concepts Learned: ✅ Prefix Sum in 2D ✅ Matrix traversal optimization ✅ Handling constraints up to 1000 × 1000 ✅ Clean implementation in Java Consistent practice is making problem-solving faster and more structured every day. #750DaysOfCode #Day536 #LeetCode #Java #DataStructures #Algorithms #PrefixSum #CodingChallenge #ProblemSolving

🚀 Day 7 / 100 – LeetCode Challenge Today I solved Length of Last Word (LeetCode #58) using Java. 🔹 Problem: Given a string "s" containing words and spaces, return the length of the last word in the string. 📌 Approach: - Traverse the string from the end. - Ignore trailing spaces. - Start counting characters of the last word. - Stop when a space appears after counting begins. 💡 Key Concepts Practiced: - String traversal - "charAt()" usage - Reverse iteration - Conditional logic ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) ✅ Key Takeaway: Sometimes solving a problem becomes easier when we traverse the data from the end instead of the beginning. #100DaysOfCode #LeetCode #Java #DSA #ProblemSolving #CodingJourney

🚀 Day 98 - #100DaysOfCode Today’s problem was all about string comparison and operations simulation in Java. 💡 Problem Insight: Given a list of operations like "++X", "X++", "--X", "X--", we need to compute the final value of X after performing all operations. ⚠️ One key learning today: In Java, always use .equals() for string comparison instead of ==. Using == compares references, not actual content — a very common mistake! 🧠 Approach: Initialize x = 0 Traverse through each operation Increment or decrement based on the operation string 📌 What I Improved Today: Better understanding of string handling in Java Avoiding common pitfalls in comparisons Writing cleaner conditional logic #Java #CodingJourney #LeetCode #100DaysOfCode #ProblemSolving #Consistency

🚀 Day 15 of #50DaysLeetCode Challenge Today I solved the “Longest Common Prefix” problem on LeetCode using Java. 🔹 Problem: Find the longest common prefix among an array of strings. If no common prefix exists, return an empty string "". Example: Input: ["flower","flow","flight"] Output: "fl" Input: ["dog","racecar","car"] Output: "" 🔹 Approach I Used: ✔ Took the first string as the initial prefix ✔ Compared it with each string in the array ✔ If mismatch occurs, reduced the prefix step by step ✔ Continued until all strings share the same prefix 🔹 Key Insight: You don’t need complex logic — just keep shrinking the prefix until it matches all strings. 🔹 Concepts Practiced: • String manipulation • Iterative comparison • Edge case handling #LeetCode #DSA #Java #Algorithms #ProblemSolving #CodingChallenge #50DaysOfCode