LeetCode 1415: Kth Lexicographical String of Happy Strings

🚀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 17/30– DSA Challenge 📌 LeetCode Problem – Plus One 📝 Problem Statement You are given a large integer represented as an array of digits. Increment the integer by one and return the resulting array. 📌 Example Input: digits = [1,2,3] Output: [1,2,4] 📌 Edge Case Input: [9,9,9] Output: [1,0,0,0] 💡 Key Insight Addition starts from the last digit. 👉 If digit < 9 → just add 1 and return 👉 If digit == 9 → it becomes 0 and carry moves left If all digits are 9 → create a new array. 🚀 Algorithm 1️⃣ Traverse from right → left 2️⃣ If digit < 9: Increment it Return array 3️⃣ Else: Set digit = 0 Continue 4️⃣ If loop ends → all were 9 Create new array of size n+1 Set first element = 1 ✅ Java Code (Optimal O(n)) class Solution { public int[] plusOne(int[] digits) { for (int i = digits.length - 1; i >= 0; i--) { if (digits[i] < 9) { digits[i]++; return digits; } digits[i] = 0; } // All digits were 9 int[] result = new int[digits.length + 1]; result[0] = 1; return result; } } ⏱ Complexity Time Complexity: O(n) Space Complexity: O(1) (except edge case) 📚 Key Learnings – Day 17 ✔ Handle carry carefully ✔ Think from right to left ✔ Edge cases matter more than logic here ✔ Small problems test attention to detail Simple idea. Tricky edge case. Clean implementation. Day 17 completed. Consistency continues 💪🔥 #30DaysOfCode #DSA #Java #InterviewPreparation #ProblemSolving #CodingJourney #Arrays #LeetCode

🚀 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 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 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

🔥 𝗗𝗮𝘆 𝟵𝟯/𝟭𝟬𝟬 — 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 𝟭𝟲𝟬𝟴. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗿𝗿𝗮𝘆 𝗪𝗶𝘁𝗵 𝗫 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝗚𝗿𝗲𝗮𝘁𝗲𝗿 𝗧𝗵𝗮𝗻 𝗼𝗿 𝗘𝗾𝘂𝗮𝗹 𝗫 | 🟢 Easy | Java A self-referential condition — x elements must be ≥ x. Elegant problem, elegant solution. 🎯 🔍 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 Find x such that exactly x elements in the array are ≥ x. Return -1 if no such x exists. ⚡ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 — 𝗙𝗿𝗲𝗾𝘂𝗲𝗻𝗰𝘆 𝗖𝗼𝘂𝗻𝘁 + 𝗦𝘂𝗳𝗳𝗶𝘅 𝗦𝘂𝗺 ✅ Cap all values at n (array length) — anything larger contributes the same way ✅ Build a frequency count array of size n+1 ✅ Traverse from right to left, accumulating a running suffix sum ✅ When suffix sum == current index i → x = i is the answer! 💡 𝗪𝗵𝘆 𝗰𝗮𝗽 𝗮𝘁 𝗻? x can never exceed n (can't have more elements than the array size). So values above n are equivalent — capping them avoids index overflow and keeps the logic clean. 📊 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 ⏱ Time: O(n) — two passes 📦 Space: O(n) — frequency array No sorting. No binary search. Just a clever frequency count + suffix accumulation. Sometimes the cleanest approach is right under your nose. 🧠 📂 𝗙𝘂𝗹𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗻 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/gm2c4-6x 𝟳 𝗺𝗼𝗿𝗲 𝗱𝗮𝘆𝘀. 𝗦𝗼 𝗰𝗹𝗼𝘀𝗲 𝘁𝗼 𝟭𝟬𝟬! 💪 #LeetCode #Day93of100 #100DaysOfCode #Java #DSA #Arrays #FrequencyCount #CodingChallenge #Programming

💻 Day 9/100 – LeetCode Challenge Today I tackled LeetCode 344 – Reverse String (Easy). The task: Reverse a character array in-place, without using extra space. I practiced: ✅ Two-pointer technique ✅ Array manipulation ✅ In-place modifications Java Solution (Two-pointer method): class Solution { public void reverseString(char[] s) { int left = 0, right = s.length - 1; while(left < right){ char temp = s[left]; s[left] = s[right]; s[right] = temp; left++; right--; } } } Example: Input: ['h','e','l','l','o'] → Output: ['o','l','l','e','h'] Takeaways: Two pointers from ends → swap toward the center In-place reversal saves memory (O(1) extra space) Great foundation for problems like rotate array, palindrome checks, and reverse linked lists #100DaysOfCode #LeetCode #Java #CodingChallenge #TwoPointers #Day9

🚀 Day 21/30 – DSA Challenge 📌 LeetCode Problem – Median of Two Sorted Arrays 📝 Problem Statement Given two sorted arrays nums1 and nums2, find the median of the combined array. 📌 Example Input: nums1 = [1,2] nums2 = [3,4] Output: 2.5 💡 My Approach Instead of using complex binary search, I followed a simple and reliable method: 👉 Merge both arrays 👉 Sort the merged array 👉 Find the median This approach is easy to understand and implement. 🚀 Algorithm 1️⃣ Create a new array of size n1 + n2 2️⃣ Copy elements of both arrays 3️⃣ Sort the merged array 4️⃣ If length is odd → return middle element 5️⃣ If even → return average of two middle elements ✅ Java Code import java.util.Arrays; class Solution { public double findMedianSortedArrays(int[] nums1, int[] nums2) { int n1 = nums1.length; int n2 = nums2.length; int[] merged = new int[n1 + n2]; for (int i = 0; i < n1; i++) { merged[i] = nums1[i]; } for (int i = 0; i < n2; i++) { merged[n1 + i] = nums2[i]; } Arrays.sort(merged); int n = merged.length; if (n % 2 == 1) { return merged[n / 2]; } else { return (merged[n / 2 - 1] + merged[n / 2]) / 2.0; } } } ⏱ Complexity Time Complexity: O((n + m) log(n + m)) Space Complexity: O(n + m) 📚 Key Learnings – Day 21 ✔ Simple solutions are often easiest to implement ✔ Always understand problem constraints ✔ This problem can be optimized further using binary search ✔ Multiple approaches exist — choose based on context Simple approach. Clear logic. Strong understanding. Day 21 completed. Consistency continues 💪🔥 #30DaysOfCode #DSA #Java #InterviewPreparation #ProblemSolving #CodingJourney #Arrays #LeetCode

🚀 Day 11 of #50DaysLeetCode Challenge Today I tackled a HARD problem: “Regular Expression Matching” on LeetCode using Java. 🔹 Problem: Given a string "s" and a pattern "p", implement regex matching with support for: • "." → matches any single character • "*" → matches zero or more of the preceding element Return true if the entire string matches the pattern. Example: Input: "s = "aa", p = "a*"" → Output: "true" Input: "s = "aa", p = "a"" → Output: "false" 🔹 Approach I Used: I used Dynamic Programming (DP) — anything else here is inefficient or breaks on edge cases. ✔ Created a 2D DP table "dp[m+1][n+1]" ✔ "dp[i][j]" represents whether "s[0...i-1]" matches "p[0...j-1]" ✔ Handled "*" carefully: - Treat it as zero occurrence → "dp[i][j-2]" - Or one/more occurrence → match previous character ✔ Built the solution bottom-up 🔹 Concepts Practiced: • Dynamic Programming (2D DP) • String pattern matching • Handling complex edge cases #LeetCode #DSA #Java #DynamicProgramming #Algorithms #CodingChallenge #50DaysOfCode

LeetCode 1593 – Split a String Into the Max Number of Unique Substrings An ideal problem to understand Backtracking. The task is simple but tricky: Split a string into substrings such that all substrings are unique, and maximize the number of splits. 🚀 Approach (Backtracking) 1. Start from index i and try every possible substring s[i...j]. 2. If the substring is not already used (checked using a HashSet), we: add it to the set recursively explore the remaining string starting from j + 1 3. Each recursive call increases the current split count. 4. When we reach the end of the string (i >= length), we update the maximum number of unique splits. 5. After recursion, we remove the substring from the set (backtrack) so other possibilities can be explored. # Core Backtracking Pattern Choose → Explore → Undo Choose: Add substring to the set Explore: Recurse for the remaining string Undo: Remove substring to try other partitions Backtracking explores all possible partitions, but the HashSet ensures no substring repeats, giving the maximum valid split. Perfect example of how recursion + state tracking can systematically search the solution space. #LeetCode #Backtracking #Java #DSA #CodingInterview #Recursion