Container With Most Water Solution in Java

Day 6/100 – LeetCode Challenge 🚀 Problem: #189 Rotate Array   Difficulty: Medium   Language: Java   Approach: Array Reversal Technique   Time Complexity: O(n)   Space Complexity: O(1) 🔍 Key Insight: Instead of shifting elements one by one, the array can be rotated efficiently using a three-step reversal strategy. Steps: 1️⃣ Reverse the entire array   2️⃣ Reverse the first k elements   3️⃣ Reverse the remaining elements  This achieves the required rotation in-place with constant extra space. 🧠 Solution Brief: Calculated k % n to handle cases where k is greater than array length.   Reversed the entire array first.   Then reversed the first k elements and the remaining n-k elements.   This sequence correctly rotates the array to the right. 📌 What I Learned: Understanding patterns like array reversal can simplify problems that initially seem complex.   Optimizing from brute force shifting to an in-place O(n) solution improves efficiency. #LeetCode #Day6 #100DaysOfCode #Java #DSA #Arrays #RotateArray #ProblemSolving #CodingJourney

Day 8 Today I solved “Check if Binary String Has at Most One Segment of Ones” on LeetCode. At first glance, the problem looks simple: Given a binary string, check whether it contains only one continuous segment of '1's. Example: "110" → Valid (one segment of 1s) "1001" → Invalid (two separate segments of 1s) While solving it, I realized an interesting observation: 👉 If a binary string has more than one segment of 1s, the pattern "01" must appear before another "1". So instead of counting segments explicitly, we can simply check whether '01' appears and is followed by another '1'. This turns the problem into a very efficient linear scan with O(n) time complexity. 💡 Key takeaway: Sometimes the simplest solution comes from recognizing patterns in the string rather than simulating the whole process. Also happy to see my solution running at 0 ms runtime (100% faster) 🚀 #LeetCode #DSA #ProblemSolving #Java #CodingJourney #BinaryStrings

🚀 Day 41/180 | #180DaysOfCode 📍 LeetCode | 💻 Java Solved: 3427. Sum of Variable Length Subarrays Used a nested loop approach to calculate subarray sums for each index by dynamically determining the starting point using max(0, i - nums[i]). ⏱️ Time Complexity: O(n²) 📦 Space Complexity: O(1) Strengthening understanding of subarray problems and index-based range calculations. 💪 Consistency continues 🚀 #DSA #LeetCode #Java #CodingJourney #Consistency

🚀 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

LeetCode Problem || Check if Binary String Has at Most One Segment of Ones(1784)🚀 we need to check: The string should have only one continuous block of '1's. After a '0' appears, '1' should never appear again. ✨ Insight: Instead of manually counting segments using loops, we can simply check if "01" exists in the string. 📌 Time Complexity: O(n) Practicing problems like these helps improve pattern recognition and problem-solving efficiency. #LeetCode #DSA #Java #CodingPractice #ProblemSolving

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

🚀 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 20/180 | #180DaysOfCode 📍 LeetCode | 💻 Java Solved: 350. Intersection of Two Arrays II Used sorting + two-pointer technique to efficiently find common elements appearing in both arrays while maintaining correct frequency. ⏱️ Time Complexity: O(n log n + m log m) 📦 Space Complexity: O(min(n, m)) (for storing the intersection) Strengthening understanding of array traversal and two-pointer pattern through consistent practice. 💪 Consistency keeps the progress moving 🚀 #DSA #LeetCode #Java #CodingJourney #Consistency

🚀 #60DaysOfLeetCode – Day 32 Today’s problem was about removing all adjacent duplicates in a string. 🔹 Approach Used: Stack To efficiently handle consecutive duplicates, I used a stack-based approach: Traverse the string character by character. If the stack is not empty and the top element matches the current character, pop it (this removes the duplicate pair). Otherwise, push the current character onto the stack. Finally, build the result string from the stack and reverse it to maintain the correct order. 🔹 Key Learning: Using a stack helps simulate the process of removing adjacent duplicates in a single pass, achieving O(n) time complexity and avoiding unnecessary reprocessing. This problem highlights how stacks are powerful for handling sequential patterns and cancellations, especially in string manipulation problems. On to Day 33! 💻🔥 #LeetCode #DSA #Java #CodingChallenge #ProblemSolving #LearningInPublic #60DaysOfCode

🚀 Day 89 of My 100 Days LeetCode Challenge | Java Today’s problem was an interesting mix of string manipulation, sliding window, and pattern matching. The challenge was to determine the minimum number of flips required to convert a binary string into an alternating sequence, considering that the string can also be rotated. At first glance it feels like a simple flipping problem, but rotation adds another layer of complexity. The key idea was to simulate all rotations efficiently by doubling the string and using a sliding window to track mismatches against alternating patterns. Instead of checking every rotation individually, this approach allows us to compute the answer in linear time. ✅ Problem Solved: Minimum Number of Flips to Make the Binary String Alternating ✔️ All test cases passed (65/65) ⏱️ Runtime: 19 ms 🧠 Approach: Sliding Window + Pattern Matching 🧩 Key Learnings: ● Rotations can often be simulated by doubling the string. ● Sliding window techniques help avoid recomputation. ● Comparing with expected patterns simplifies mismatch counting. ● Efficient string handling is crucial for optimization problems. ● Sometimes the trick is not the operation itself, but how you simulate it efficiently. This problem was a great reminder that smart transformations can turn complex problems into linear-time solutions. 🔥 Day 89 complete — improving my understanding of sliding window techniques and string optimizations. #LeetCode #100DaysOfCode #Java #SlidingWindow #Strings #ProblemSolving #DSA #CodingJourney #Consistency