LeetCode Challenge: Alternating Pattern Mismatches

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/gSiw_GQq 💡 My thought process: It uses a two-pointer approach. One pointer starts at the top row of the submatrix, and the other starts at the bottom row. For each pair of rows, it goes through all columns within the submatrix range and swaps the elements column by column. This process continues until the top and bottom pointers meet or cross. This ensures the submatrix is reversed in place without using any extra space. The updated grid is then returned. 👉 My Solution: https://lnkd.in/g_cAfrtb If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/gx6PQVMB 💡 My thought process: It is mentioned that the size of the array also denotes the size of each string and the number of unique strings in the array. I took an unordered set, and I generated all the possible binary strings of length n. I only generated n+1 strings because we only need to return any one of them. When the size of the set goes above n, I stop finding anymore binary strings. Then, I iterate over the array and remove those binary strings present in the array from the set. Just simply return the remaining string in the set. 👉 My Solution: https://lnkd.in/gJKMj6vX If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/gBvd5iWy 💡 My thought process: For this question, I am precalculating the total sum of the grid and row-wise and column-wise sums. Store the precalculated values in a vector and then simply do a cumulative sum over the vector. If the cumulative sum equals the total sum - cumulative sum, this means that a single cut is possible to split the array. The calculation part is done in another method, and I simply call the method twice for row-wise check and then column-wise. 👉 My Solution: https://lnkd.in/gNYsk6iz If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/g6uRPkE6 💡 My thought process: The first approach calculates the bitwise complement by examining each bit of the number. It starts by determining how many bits are needed to represent n using log2(n) + 1. For each bit position i, a mask (1 << i) is used to isolate that bit from n. If the extracted bit is 0, the result for that position is set by adding the same mask, effectively flipping the bit. This creates a new number where all bits within the effective bit length of n are inverted, resulting in the complement. The second approach uses the XOR operation to flip all relevant bits at once. It first calculates the number of bits in n and creates a mask filled with 1s in all those positions using (1 << digits) - 1. This mask represents the highest value that can be formed with the same number of bits. Applying n ^ mask flips every bit within that range because XOR with 1 changes the bit and XOR with 0 leaves it the same, giving the bitwise complement of n. 👉 My Solution: https://lnkd.in/gGCdUW3H If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/geeKcuMP 💡 My thought process: The approach counts the number of special positions in a binary matrix by ensuring that a cell containing 1 is the only 1 in both its row and column. First, the number of rows, m, and columns, n, are determined. Two auxiliary arrays, rowOnes and colOnes, store the count of 1s in each row and column, respectively. The matrix is first traversed row by row to count how many 1s appear in each row and store these counts in rowOnes. Next, it is traversed column by column to count how many 1s appear in each column and store these counts in colOnes. After computing these counts, the matrix is scanned again. For every cell containing 1, it checks whether that row and column both contain exactly one 1. If both conditions are met, the position is considered special, and the count is increased. Finally, the total number of such positions is returned. 👉 My Solution: https://lnkd.in/g9eZdbF2 If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/gx6PQVMB 💡 My thought process: The brute force method checks all possible rotations of the binary string by moving the first character to the end repeatedly. For each rotated string, it counts the number of mismatches needed to create an alternating pattern that starts with 1. The opposite alternating pattern, which starts with 0, would require different changes. The method takes the minimum of the two counts for each rotation and returns the lowest value overall. The optimized method does not generate all rotations. Instead, it doubles the string and uses a sliding window of size n to represent every possible rotation. It keeps track of the mismatch count for forming an alternating pattern that starts with 1. As the window slides, it updates this count by subtracting the contribution of the character that leaves the window and adding the incoming character. At each step, it compares both alternating patterns to find the minimum number of flips needed. 👉 My Solution: https://lnkd.in/gP2FWVYP If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

Today’s LeetCode POTD (Hard) turned out to be a great reminder: Not every Hard problem demands complex algorithms. Sometimes, it's just about clear thinking and careful implementation. This one was mainly about: • Handling constraints step-by-step • Managing overlaps correctly • Staying consistent with the required conditions 👉 Don’t get intimidated by the difficulty tag — focus on understanding the problem. My approach: • First enforced all required matches (T) • Then filled remaining positions greedily (smallest chars) • Finally ensured F conditions by introducing minimal mismatches without breaking fixed parts Consistency > Complexity. Leetcode Id: https://lnkd.in/dvMsSxVw #leetcode #dsa #coding #problemSolving #consistency

🚀 LeetCode Daily Challenge 🔗 Problem: https://lnkd.in/gbtaBXYW 💡 My thought process: The algorithm first calculates a normalized shift value using k % n. Shifting a row by its total length, n, returns the original configuration. Therefore, any shift k is the same as k modulo n. Instead of rotating memory physically or creating a temporary matrix, the code uses a virtual mapping strategy. It goes through each cell (i, j) and finds the index of the element that would be in that position after the shift. For even rows (left shift), to find the original value that moves into position j after a left shift, the code looks at the index (j - shift). It manages negative results by wrapping around with an addition of n. For odd rows (right shift), it checks the index (j + shift) % n. The function has a fail-fast mechanism; it returns false right away if it finds the first difference between a cell and its shifted counterpart. 👉 My Solution: https://lnkd.in/g5myRYRj If you found this breakdown helpful, feel free to ⭐ the repo or connect with me on LinkedIn 🙂🚀 #️⃣ #leetcode #cpp #dsa #coding #problemsolving #engineering #BDRM #BackendDevWithRahulMaheswari

🚀 Day 2 of #100DaysOfCode 📌 Reverse Integer 🔗 https://lnkd.in/gtKB8Mdi ✅ Accepted (All test cases passed) ⚡ Runtime: 0 ms 🧠 Approach: Reversed the number digit by digit using modulo and division, while handling overflow conditions carefully. ⏱️ Time: O(log n) | 📦 Space: O(1) 💡 Learning: Handling edge cases like overflow is the key part of this problem. #leetcode #dsa #cpp #coding #developers

🚀 Do you solve array problems randomly… or with a plan? Recently, I worked on 16 array problems in Java but this time, I didn’t just jump from one question to another. I followed a clear approach. Instead of solving randomly, I broke it down like this: 🔹 Problems 1–6 → Simple array traversal (Counting, sum, product, basic operations) 🔹 Problems 7–12 → Min–Max tracking (Largest, smallest, kth elements, second largest/smallest) 🔹 Problems 13–16 → Array transformation (Two-pointer approach) (Copy, reverse, left rotation, right rotation) At first, arrays felt easy… just loops. But while solving these step by step, I realized something 👉 The real challenge is not coding, it’s how you approach the problem. This practice helped me: ✔ Think more clearly before writing code ✔ Understand when to use which approach ✔ Improve my problem-solving flow ✔ Build confidence in handling DSA basics Honestly, this didn’t just improve my coding… it changed the way I look at problems now. Grateful for the learning environment at Global Quest Technologies and for the constant guidance from sir G.R NARENDRA REDDY (CEO, GQT). #Java #DSA #Arrays #CodingPractice #ProblemSolving #LearningJourney #GlobalQuestTechnologies