Finding Median of Two Sorted Arrays with Java Solution

Day 25/100: Finding the "Gap" 🎯 Today's challenge: Search Insert Position. We all know Binary Search finds an element in O(log n), but what if the element isn't there? I learned that by the end of the search, the `left` pointer doesn't just give up—it points exactly to where that missing number *should* be inserted to keep the array sorted. It’s a powerful way to handle dynamic data without breaking the order. Quarter of the way through the challenge! 🚀 #100DaysOfCode #Java #DSA #BinarySearch #ProblemSolving #Unit3 #Day25 #LearnInPublic

This problem is labeled Hard... But my approach wasn't. 🚀 Day 79/365 — DSA Challenge Median of Two Sorted Arrays The requirement says: ⚡ Solve in O(log (m+n)) But today... I focused on clarity over optimization 💡 My Approach: 1. Merge both sorted arrays 2. Find the median from the merged array While merging: • Compare elements from both arrays • Add the smaller one • Continue until fully merged Then: If total length is odd → pick middle If even → take average of two middle values ⏱ Time: O(m + n) 📦 Space: O(m + n) What I learned: You don't always need the optimal solution first. A clear solution builds understanding. Optimization can come next. Code 👇 https://lnkd.in/dad5sZfu #DSA #Java #LeetCode #LearningInPublic #Consistency #ProblemSolving

𝐃𝐚𝐲 87/100 – 𝐋𝐞𝐞𝐭𝐂𝐨𝐝𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 🚀 Problem: 228. 𝐒𝐮𝐦𝐦𝐚𝐫𝐲 𝐑𝐚𝐧𝐠𝐞𝐬  Today I solved a problem where we need to summarize consecutive numbers in a sorted unique array into ranges. 🔑 𝐈𝐝𝐞𝐚: Traverse the array and keep extending the range while consecutive numbers continue. Once the sequence breaks, close the range and store it. 💡 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡: Start with the first element as start Move forward while nums[i] + 1 == nums[i+1] If range exists → "start->end" Else → single number "start" 𝐊𝐞𝐲 𝐈𝐧𝐬𝐢𝐠𝐡𝐭: Efficient single pass solution (O(n)) by grouping consecutive elements on the fly. #LeetCode #Java #ProblemSolving #DSA #100DaysOfCode #CodingJourney

Day 16/100 – LeetCode Challenge Problem: Merge Sorted Array Today’s problem involved merging two sorted arrays into one sorted array. Approach: Created a temporary array of size m + n Used two pointers to compare elements from both arrays Inserted the smaller element into the new array Copied remaining elements if any array still had values Finally copied the merged result back into nums1 Complexity: Time: O(m + n) Space: O(m + n) Concepts Practiced: Two-pointer technique Array traversal Merging sorted arrays #100DaysOfCode #LeetCode #DSA #Java #Arrays #ProblemSolving #CodingJourney

Day 72 of #100DaysOfCode Problem: Convert Sorted Array to Height-Balanced BST Today I learned how to efficiently convert a sorted array into a balanced Binary Search Tree using Divide & Conquer. Key Insight: Pick the middle element as the root to maintain balance. Recursively build: Left subtree from left half Right subtree from right half This ensures: Optimal height Faster search operations ⏱ Time Complexity: O(n) 📦 Space Complexity: O(log n) Consistency is the real game changer #DSA #Java #BinaryTree #LeetCode #CodingJourney

Day 41 - Find Pivot Index Solved using a prefix sum approach. First computed the total sum of the array, then traversed once while maintaining a running left sum. For each index, the right sum is calculated using total - left - nums[i]. If both sums match, that index is the pivot. Time Complexity: O(n) #Day41 #LeetCode #Java #PrefixSum #DSA #ProblemSolving #CodingJourney

🚀 Day 41 of #100DaysOfCode Solved 189. Rotate Array on LeetCode 🔄 🧠 Key Insight: Rotating an array by k steps to the right means the last k elements move to the front, while the remaining elements shift to the right. ⚙️ Approach: 1️⃣ Compute effective rotations using k % n 2️⃣ Store the last k elements in a temporary array 3️⃣ Shift the remaining n-k elements to the right 4️⃣ Copy the stored elements back to the beginning of the array This ensures the rotation happens in-place with controlled extra space. ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(k) #100DaysOfCode #LeetCode #DSA #Arrays #Java #ProblemSolving #InterviewPrep #LearningInPublic

🚀 Day 49 of #100DaysOfCode Solved 86. Partition List on LeetCode 🔗 🧠 Key Insight: We need to rearrange a linked list such that: 🔹All nodes < x come before nodes ≥ x 🔹While maintaining the original relative order This is a classic linked list partitioning problem. ⚙️ Approach: 1️⃣ Create two dummy lists: 🔹small → nodes with values < x 🔹large → nodes with values ≥ x 2️⃣ Traverse the original list: 🔹Append each node to the appropriate list 3️⃣ Connect both lists: 🔹small → large 4️⃣ Return the head of the new list 🎯 This ensures: 🔹Stable ordering 🔹Clean and efficient separation ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(1) (no extra nodes, just pointers) #100DaysOfCode #LeetCode #DSA #LinkedList #Algorithms #Java #CodingPractice #InterviewPrep

🚀 Day 35 of my #100DaysOfCode Journey Today, I solved the LeetCode problem: Valid Anagram Problem Insight: Given two strings, check if one is an anagram of the other. Approach: • First, check if the strings have the same length; if not, return false • Convert both strings to character arrays • Sort both arrays • Compare the sorted arrays — if equal, the strings are anagrams Time Complexity: • O(n log n) — due to sorting the arrays Space Complexity: • O(n) — for the character arrays Key Learnings: • Sorting is a simple and effective way to compare character compositions • Edge cases like different lengths should be handled first • Breaking the problem into small steps makes it easy to reason about Takeaway: Sometimes, sorting can reduce a seemingly complex problem into a simple comparison. #DSA #Java #LeetCode #100DaysOfCode #CodingJourney #ProblemSolving #Strings

𝐃𝐚𝐲 𝟓𝟔 – 𝐃𝐒𝐀 𝐉𝐨𝐮𝐫𝐧𝐞𝐲 | 𝐀𝐫𝐫𝐚𝐲𝐬 🚀 Today’s problem focused on finding a peak element using binary search. 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐒𝐨𝐥𝐯𝐞𝐝 • Find Peak Element 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 • Used binary search instead of linear scan • Compared the middle element with its next element Logic: • If nums[mid] > nums[mid + 1] → peak lies on the left side (including mid) • Else → peak lies on the right side • Continued until left == right 𝐊𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬 • Binary search can be applied on patterns, not just sorted arrays • A peak always exists due to problem constraints • Comparing adjacent elements helps determine direction • Reducing the search space is the key idea 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 • Time: O(log n) • Space: O(1) 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 Binary search is not about sorted arrays — it’s about eliminating half of the search space using logic. 56 days consistent 🚀 On to Day 57. #DSA #Arrays #BinarySearch #LeetCode #Java #ProblemSolving #DailyCoding #LearningInPublic #SoftwareDeveloper