N-Queens Problem: Backtracking with Efficient Conflict Checking

Day 69/100 | #100DaysOfDSA 🧩🚀 Today’s problem: Reverse Nodes in k-Group A challenging linked list problem focused on reversing nodes in fixed-size groups. Problem idea: Reverse nodes of a linked list in groups of size k, while keeping remaining nodes as it is. Key idea: Linked list manipulation + iterative reversal in segments. Why? • We need to reverse nodes in chunks, not the whole list • Careful pointer handling is required • Must preserve connections between groups How it works: • Use a dummy node for easier handling of head • Identify the k-th node from current position • Reverse nodes between current and k-th node • Connect reversed group back to the list • Move to next group and repeat • Stop if remaining nodes are less than k Time Complexity: O(n) Space Complexity: O(1) Big takeaway: Mastering pointer manipulation in linked lists is key to solving advanced problems efficiently. This problem is a great example of combining reversal logic with structural control. 🔥 Day 69 done. 🚀 #100DaysOfCode #LeetCode #DSA #Algorithms #LinkedList #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity

Day 79/100 | #100DaysOfDSA 🧩🚀 Today’s problem: Flatten Binary Tree to Linked List A powerful tree transformation problem using pointer manipulation. Problem idea: Convert a binary tree into a linked list in-place following preorder traversal. Key idea: Iterative traversal + rewiring (similar to Morris traversal idea). Why? • We need preorder sequence (Root → Left → Right) • Instead of extra space, we modify pointers in-place • Efficient and avoids recursion stack How it works: • Traverse using a pointer curr • If left child exists: → Find rightmost node of left subtree → Connect it to current’s right subtree → Move left subtree to right → Set left = null • Move to next node (curr.right) Time Complexity: O(n) Space Complexity: O(1) Big takeaway: Tree problems can often be optimized using in-place pointer rewiring, avoiding extra space. 🔥 This pattern is very useful for tree flattening and traversal optimizations. Day 79 done. 🚀 #100DaysOfCode #LeetCode #DSA #Algorithms #BinaryTree #MorrisTraversal #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity

🧠 Day 206 — Minimum Distance to Target 🎯📍 Today solved a simple yet important problem and did some revision alongside. 📌 Problem Goal Given an array, a target value, and a starting index: ✔️ Find the minimum distance between the start index and any index where the target exists 🔹 Core Idea Traverse the array and: ✔️ Check if current element matches the target ✔️ Calculate distance from the start index ✔️ Keep updating the minimum distance 🔹 Key Observation ✔️ Only indices with the target matter ✔️ Distance = absolute difference between indices ✔️ Simple linear scan gives optimal solution 🧠 Key Learning ✔️ Even easy problems strengthen fundamentals ✔️ Brute-force with clarity > overthinking ✔️ Absolute difference patterns are very common in arrays 💡 Today’s Realization Not every day needs to be a hard problem. Consistency with simple + revision days builds stronger intuition. 🚀 Momentum Status: Staying consistent and sharpening basics. On to Day 207. #DSA #Arrays #ProblemSolving #LeetCode #Java #CodingJourney #ConsistencyWins

💡 LeetCode 191 — Number of 1 Bits (Hamming Weight) Recently solved an interesting bit manipulation problem that highlights the power of low-level optimization 🚀 🔍 Problem Statement: Given an unsigned integer, count the number of set bits (1s) in its binary representation. 🧠 Key Insight: Instead of checking every bit individually, we can use a clever trick: 👉 n & (n - 1) removes the rightmost set bit in each operation. This allows us to count only the set bits, making the solution more efficient. ⚙️ Approach Used (Brian Kernighan’s Algorithm): Initialize a counter Repeatedly apply n = n & (n - 1) Increment count until n becomes 0 📈 Time Complexity: O(k), where k = number of set bits (faster than checking all 32 bits) 📦 Space Complexity: O(1) ✨ Why this problem is important: Strengthens understanding of bit manipulation Frequently asked in interviews Useful in low-level optimization and system design 💬 Takeaway: Sometimes the best solutions come from understanding how data is represented at the binary level. Small tricks can lead to big optimizations! #LeetCode #DSA #CodingInterview #Java #BitManipulation #ProblemSolving #TechJourney

✅ Solved LeetCode 217 — Contains Duplicate! Given an integer array nums, return true if any value appears at least twice. 🧠 My Approach: Sort + Linear Scan → Sort the array → adjacent duplicates are guaranteed to be neighbors → One pass to check if nums[i] == nums[i-1] → Time: O(n log n) | Space: O(1) 💡 Key Insight: Sorting brings duplicates side by side — no need for extra space like a HashSet. Trade time for space! ```java Arrays.sort(nums); for (int i = 1; i < nums.length; i++) { if (nums[i] == nums[i - 1]) return true; } return false; ``` 🔄 Alternative approaches: • HashSet → O(n) time, O(n) space • Brute force → O(n²) time (avoid!) Every problem teaches you a new trade-off. Keep grinding! 💪 #LeetCode #DSA #CodingInterview #Java #ProblemSolving #SoftwareEngineering #100DaysOfCode #Programming

Day 70 of My DSA Journey Problem: Linked List Cycle (LeetCode 141) Today’s problem was all about detecting whether a linked list contains a cycle — a classic and very important concept in data structures. Key Idea: Floyd’s Cycle Detection Algorithm (Tortoise & Hare) Instead of using extra memory, we use two pointers: • Slow pointer → moves 1 step • Fast pointer → moves 2 steps If there’s a cycle, these two pointers will eventually meet. If not, the fast pointer will reach the end (null). Insight: This approach is efficient because it avoids using extra space like a HashSet and still guarantees detection in linear time. Complexity: • Time: O(n) • Space: O(1) What I learned: Sometimes, the smartest solutions don’t require extra space — just a clever observation and pointer manipulation! Consistency > Perfection 💯 On to Day 71 🚀 #DSA #100DaysOfCode #Java #CodingJourney #ProblemSolving #LinkedList

📘 DSA Journey — Day 29 Today’s focus: Binary Search on answer space. Problem solved: • Find Peak Element (LeetCode 162) Concepts used: • Binary Search • Observing slope / trend • Search space reduction Key takeaway: The goal is to find a peak element (an element greater than its neighbors). Instead of checking all elements, we use binary search by observing the slope: • If nums[mid] < nums[mid + 1] → we are on an increasing slope, so a peak must exist on the right side • Else → we are on a decreasing slope, so a peak lies on the left side (including mid) By following this logic, we eliminate half of the search space each time and find a peak in O(log n) time. The key insight is: A peak is guaranteed to exist, and the direction of slope helps guide the search. Continuing to strengthen binary search intuition and consistency in problem solving. #DSA #Java #LeetCode #CodingJourney

Day 68/100 | #100DaysOfDSA 🧩🚀 Today’s problem: Add Two Numbers A fundamental linked list problem that mimics real-life addition. Problem idea: Add two numbers represented by linked lists (digits stored in reverse order). Key idea: Linked list traversal + carry handling. Why? • We process digits one by one (like manual addition) • Need to handle carry at each step • Lists can have different lengths How it works: • Use a dummy node to build the result • Traverse both lists simultaneously • Add values + carry • Create new node with (sum % 10) • Update carry = sum / 10 • Move pointers forward • Continue until both lists and carry are done Time Complexity: O(max(m, n)) Space Complexity: O(max(m, n)) Big takeaway: Using a dummy node simplifies linked list construction and avoids edge cases. This pattern is very common in linked list problems. 🔥 Day 68 done. 🚀 #100DaysOfCode #LeetCode #DSA #Algorithms #LinkedList #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity

Day 85/100 🚀 | #100DaysOfDSA Solved LeetCode 300 – Longest Increasing Subsequence (LIS) today. Initially, the brute force / DP approach (O(n²)) is intuitive, but I implemented the optimized O(n log n) solution using Binary Search. Approach (Patience Sorting Idea): Used an array tails[] where: • tails[i] = smallest possible tail of an increasing subsequence of length i+1 For each number: • Used binary search to find its correct position in tails • Replaced the element at that position • If it extends the largest subsequence → increased size ⚠️ Important: tails does NOT store the actual subsequence, but helps compute the length efficiently. Time Complexity: O(n log n) Space Complexity: O(n) Key takeaway: When you see “longest increasing subsequence”, think: • O(n²) → DP • O(n log n) → Binary Search + Greedy (optimal) This is a classic interview problem and a big step up from basic questions — good progress. #100DaysOfDSA #LeetCode #DSA #Java #DynamicProgramming #BinarySearch #Greedy #ProblemSolving #Consistency

#Day362 of #1001DaysOfCode 📘 LeetCode Daily Challenge Problem: XOR After Queries 💡 Approach: Optimized the brute-force simulation using sqrt decomposition. Large step queries were processed directly Small step queries were grouped and batch-processed efficiently Used modular exponentiation + grouped progression updates to reduce time complexity significantly. ⏱ Optimized Time Complexity: ~O(q√n + n√n log MOD) 🧠 Space Complexity: O(n + q) A great problem for learning advanced optimization techniques 🚀 #DSA #Java #LeetCode #ProblemSolving #Algorithms #Coding