XOR After Range Multiplication Queries II Key Takeaways

🚀 Day 128/500 – DSA LeetCode Challenge Today I solved problems focused on circular arrays and greedy logic. ✅ Closest Target in Circular Array – Used linear scan + circular distance calculation to find the minimum steps. TC: O(n) | SC: O(1) ✅ Partitioning Into Minimum Number of Deci-Binary Numbers – Applied a greedy observation that the answer is the maximum digit in the string. TC: O(n) | SC: O(1) 💡 Key Learning: Sometimes the best solution comes from identifying the core mathematical insight, not complex logic. 👉 Day 128/500 #DSA #Java #500DaysChallenge #ProblemSolving #CodingJourney

Day 48 #SDE two-pointer techniques and cycle detection patterns. Solved: • Container With Most Water • Find the Duplicate Number Key Learning: • “Container With Most Water” is a classic two-pointer problem, where moving the smaller height pointer helps maximize area efficiently. • “Find the Duplicate Number” can be solved using Floyd’s Cycle Detection (Tortoise & Hare) without modifying the array and using O(1) space. #LeetCode #DSA #TwoPointers #Algorithms #Java #SoftwareEngineering

Day 39 #SDE Problems on mathematical optimization and dynamic programming on strings. Solved: • Count Good Numbers • Word Break Key Learning: • “Count Good Numbers” uses fast exponentiation (binary exponentiation) to efficiently compute large powers under modulo. • “Word Break” is a classic DP problem, where we check if a string can be segmented using a dictionary — reinforcing recursion + memoization patterns. #LeetCode #DSA #DynamicProgramming #Recursion #Algorithms #Java #SoftwareEngineering

Day 26 #SDE focused on sliding window and voting algorithm patterns. Solved: • Permutation in String • Majority Element II Key Learning: • “Permutation in String” uses the sliding window + frequency count approach to efficiently check if any permutation of one string exists in another. • “Majority Element II” is a great example of the Boyer-Moore Voting Algorithm, which helps find elements appearing more than ⌊n/3⌋ times in O(n) time and O(1) space. #LeetCode #DSA #SlidingWindow #Algorithms #Java #SoftwareEngineering

Day 46 #SDE prefix sum and sliding window techniques. Solved: • Binary Subarrays With Sum • Maximum Points You Can Obtain from Cards Key Learning: • “Binary Subarrays With Sum” → Two strong approaches: – Prefix sum + hashmap (counting subarrays efficiently) – At-most K sliding window → exactly(K) = atMost(K) - atMost(K-1) • “Maximum Points from Cards” → Classic optimization: – Instead of picking k elements, remove a window of size (n - k) – Convert problem into minimizing subarray sum #LeetCode #DSA #SlidingWindow #PrefixSum #Algorithms #Java #SoftwareEngineering

Day 27 #SDE Focused on array manipulation and dynamic programming patterns. Solved: • Find All Duplicates in an Array • Maximum Product Subarray Key Learning: • “Find All Duplicates in an Array” can be solved efficiently using index marking (negative marking) to achieve O(n) time and O(1) extra space. • “Maximum Product Subarray” highlights the importance of tracking both max and min products, since a negative number can flip the result. #LeetCode #DSA #Arrays #DynamicProgramming #Algorithms #Java #SoftwareEngineering

Day 44 #SDE sliding window techniques and optimization problems. Solved: • Longest Substring Without Repeating Characters • Maximum Points You Can Obtain from Cards Key Learning: • “Longest Substring Without Repeating Characters” is a classic sliding window + hashing problem, where we dynamically adjust the window to maintain unique characters. • “Maximum Points from Cards” uses a clever window optimization, where instead of picking k cards directly, we minimize the sum of the remaining subarray. #LeetCode #DSA #SlidingWindow #Algorithms #Java #SoftwareEngineering

Day 49 #SDE advanced sliding window techniques and string optimization problems. Solved: • Subarrays with K Different Integers • Minimum Window Substring Key Learning: • “Subarrays with K Different Integers” uses the at-most K sliding window technique, where we compute atMost(k) - atMost(k-1) to get the exact count. • “Minimum Window Substring” is a classic variable sliding window problem, where we expand and shrink the window while maintaining required character frequencies. #LeetCode #DSA #SlidingWindow #Strings #Algorithms #Java #SoftwareEngineering

Day 29 #SDE Focused on recursion/backtracking and array rearrangement techniques. Solved: • Number of Valid Parentheses Expressions • Rearrange Array Alternately Key Learning: • Generating valid parentheses is a classic backtracking problem, where we build combinations by maintaining constraints (open ≤ n, close ≤ open). • Rearranging arrays alternately can be optimized using two-pointer or encoding techniques to achieve in-place transformation. #LeetCode #DSA #Recursion #Arrays #Algorithms #Java #SoftwareEngineering

🚀 Day 557 of #750DaysOfCode 🚀 🔥 Solved: XOR After Range Multiplication Queries II (Hard) Today’s problem really pushed my understanding of optimization and patterns in arrays. At first glance, it looks like a simple simulation—but with constraints up to 10⁵, a brute-force approach quickly breaks down. 💡 Key Learnings: Handling range updates efficiently is crucial Observed how step-based traversal (k jumps) affects time complexity Importance of modular arithmetic in large computations XOR properties helped in deriving the final result efficiently ⚡ Challenge: Each query updates elements at intervals, making it tricky to optimize without directly iterating every time. 🧠 Takeaway: Hard problems are less about coding and more about recognizing patterns and optimizing smartly. Consistency is the real game 💯 #LeetCode #DataStructures #Algorithms #CodingJourney #ProblemSolving #Java #100DaysOfCode #KeepGrinding