Day 50/100 | #100DaysOfDSA 🚀🔥 Today’s problem: Word Search Given a grid of characters and a word, we need to check if the word exists by moving horizontally or vertically. At first it feels like simple traversal… But the catch? You can’t reuse the same cell twice. Approach: • Try starting from every cell • Use DFS + Backtracking • Match characters step by step • Mark visited cells and backtrack if path fails Time Complexity: O(m × n × 4^L) Space Complexity: O(L) (recursion stack) Big takeaway: When choices branch in multiple directions, Backtracking is the key. Half century done. Staying consistent. 💯 Day 50 complete. 🚀 #100DaysOfCode #LeetCode #DSA #Algorithms #Backtracking #DFS #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity
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Day 58/100 | #100DaysOfDSA 🔄🔍 Today’s problem: Search in Rotated Sorted Array A twist on Binary Search with a rotated array. Key idea: Even though the array is rotated, one half is always sorted. Approach: • Use binary search • Find mid element • Check which half is sorted (left or right) • Decide if target lies in the sorted half • Narrow the search accordingly Why it works: At every step, we eliminate half of the search space just like standard binary search. Time Complexity: O(log n) Space Complexity: O(1) Big takeaway: Understanding the structure of the problem helps adapt classic algorithms like binary search. Rotation doesn’t break order — it just shifts it. 🔥 Day 58 done. #100DaysOfCode #LeetCode #DSA #Algorithms #BinarySearch #Arrays #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity
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Day 57/100 | #100DaysOfDSA ⛰️🔍 Today’s problem: Peak Index in a Mountain Array A perfect use-case of Binary Search on a pattern. Key observation: The array increases → reaches a peak → then decreases. Approach: • Use binary search on the index • Compare mid with mid + 1 • If arr[mid] > arr[mid + 1] → we are on the decreasing side → move left • Else → we are on the increasing side → move right This guarantees we always move toward the peak. Time Complexity: O(log n) Space Complexity: O(1) Big takeaway: Binary search isn’t just for sorted arrays — it works on patterns too. Recognizing these patterns is a game changer. 🔥 Day 57 done. #100DaysOfCode #LeetCode #DSA #Algorithms #BinarySearch #Arrays #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity
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Day 7/30 of My #30DaysDSA Challenge Today’s problem: Sqrt(x) Problem number 69 on LeetCode At first glance, it looks simple — just find the square root, right? But the twist is: ❌ No built-in functions allowed I initially thought of a brute force approach (checking i * i <= x), but that would take O(√x) time — not efficient for large inputs. 💡 Then came the key insight: Use Binary Search to reduce the search space and achieve O(log x) time complexity. Instead of finding the exact square root, the goal was to return the floor value (nearest integer ≤ √x). Also learned an important trick to avoid overflow by using long while squaring numbers. 🔍 What I learned today: • How to apply binary search beyond sorted arrays • Optimizing from brute force → logarithmic solution • Handling edge cases like large inputs and overflow Every day, I’m realizing DSA is less about memorizing and more about thinking efficiently 🧠 #DSA #Java #BinarySearch #ProblemSolving #LearningInPublic #Consistency #100DaysOfCode
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Day 59/100 | #100DaysOfDSA 🔍⚡ Today’s problem: Single Element in a Sorted Array A clever Binary Search problem with a twist. Key observation: In a sorted array where every element appears twice except one, pairs follow a pattern. Before the single element: • First occurrence → even index • Second occurrence → odd index After the single element: • Pattern breaks Approach: • Use binary search • Check mid with its pair using index trick (mid ^ 1) • If nums[mid] == nums[mid ^ 1] → move right • Else → move left This helps pinpoint where the pattern breaks. Time Complexity: O(log n) Space Complexity: O(1) Big takeaway: Bit manipulation + pattern observation can simplify binary search problems significantly. Small tricks → big optimizations. 🔥 Day 59 done. #100DaysOfCode #LeetCode #DSA #Algorithms #BinarySearch #BitManipulation #Arrays #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity
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🔥 Day 63 of #100DaysOfCode Solved – Missing Number 🔍 What I did: Calculated the expected sum of numbers from 0 to n and subtracted the actual sum of the array to find the missing number efficiently. 💡 Key Learning: Learned how to use mathematical formula (n × (n+1) / 2) Practiced optimizing from brute force to O(n) solution Improved understanding of number patterns in arrays 🎯 Takeaway: Using mathematical insights can simplify problems and eliminate the need for extra loops or space. #Day62 #LeetCode #Java #ProblemSolving #CodingJourney #100DaysOfCode #DSA
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#100DaysOfCode – Day 2 Today I worked on a DSA problem based on arrays: Check if an array is sorted and rotated 🔍 Approach: Instead of finding the exact rotation point, I focused on identifying a pattern: In a sorted and rotated array, the order should break at most once. So, I checked how many times an element is greater than the next element while traversing the array in a circular manner. ✔️ If the count of such breaks is 0 or 1 → valid ❌ If it’s more than 1 → not a sorted rotated array 🧠 Key Takeaway: This problem taught me how pattern observation can simplify logic and avoid unnecessary complexity. Sometimes the best solution is not the most obvious one! 📈 Staying consistent and improving step by step 💪 #100DaysOfCode #DSA #DataStructures #Algorithms #Java #CodingJourney #ProblemSolving #LeetCode #Consistency
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Day 61/100 | #100DaysOfDSA 🧩🚀 Today’s problem: Median of Two Sorted Arrays A classic hard problem with a clever binary search approach. Problem idea: Find the median of two sorted arrays without fully merging them. Key idea: Use binary search on the smaller array to partition both arrays. Why? • We want left half and right half to be balanced • All elements in left ≤ all elements in right How it works: • Pick a cut in array1 • Derive cut in array2 • Check partition validity using boundary elements If valid → we found the median If not → adjust the partition Time Complexity: O(log(min(m, n))) Space Complexity: O(1) Big takeaway: Binary search isn’t just for searching — it can be used to optimize partitions and positions. This one really builds intuition. 🔥 Day 61 done. #100DaysOfCode #LeetCode #DSA #Algorithms #BinarySearch #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity
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Day 76/100 | #100DaysOfDSA 🧩🚀 Today’s problem: Lowest Common Ancestor of a Binary Tree A fundamental tree problem that builds strong recursion intuition. Problem idea: Find the lowest node in the tree that has both given nodes as descendants. Key idea: DFS + recursion (bottom-up approach). Why? • Each subtree can independently tell if it contains p or q • Combine results while backtracking • First node where both sides return non-null → answer How it works: • If current node is null / p / q → return it • Recursively search left and right subtree • If both left & right are non-null → current node is LCA • Else return the non-null side Time Complexity: O(n) Space Complexity: O(h) (recursion stack) Big takeaway: Tree problems often rely on post-order traversal + combining child results. Understanding this pattern unlocks many binary tree problems. 🔥 Day 76 done. 🚀 #100DaysOfCode #LeetCode #DSA #Algorithms #BinaryTree #Recursion #DFS #Java #CodingJourney #ProblemSolving #InterviewPrep #TechCommunity
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📌 Median of Two Sorted Arrays Platform: LeetCode #4 Difficulty: Hard ⚙️ Approach • Apply binary search on the smaller array for efficiency • Define search space from 0 to n1 • Partition both arrays such that: – Left half contains (n1 + n2 + 1) / 2 elements – Right half contains remaining elements • Identify boundary elements: – l1, l2 → left side elements – r1, r2 → right side elements • Check valid partition: – l1 <= r2 and l2 <= r1 • If valid: – For even length → median = average of max(left) and min(right) – For odd length → median = max(left) • If not valid: – If l1 > r2, move left – Else move right 🧠 Logic Used • Binary Search on partition index • Dividing arrays into balanced halves • Handling edge cases using boundary values • Achieving optimal O(log(min(n1, n2))) time complexity 🔗 GitHub: https://lnkd.in/g_3x55n8 ✅ Day 36 Completed – Revised advanced binary search and partition-based problem. #100DaysOfCode #Java #DSA #ProblemSolving #CodingJourney #Algorithms #DataStructures #LeetCode #CodingPractice #CodeEveryDay #BinarySearch #ArrayProblems
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Day 36/50 🚀 Solved “Valid Parentheses” today — a classic stack problem, but a great reminder that simple concepts can be powerful when applied correctly. 💡 Key takeaway: Using a stack makes it easy to track opening brackets and validate matching pairs efficiently. Every closing bracket should correspond to the most recent unmatched opening one — LIFO in action. ⚙️ What I focused on: Clean conditional checks Avoiding unnecessary complexity Writing readable, structured code 📈 Result: Accepted ✅ Optimized runtime & solid performance On to Day 37 🔥 #50DaysOfCode #DataStructures #Algorithms #Java #LeetCode #CodingJourney
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