Binary Search in Rotated Sorted Array

🚀 Day 38 of #100DaysOfCode Solved 154. Find Minimum in Rotated Sorted Array II on LeetCode 🔍 🧠 Key Insight: The array is sorted but rotated, and this version introduces duplicates, which makes the binary search logic slightly trickier. ⚙️ Approach: 🔹Use binary search with left and right pointers 🔹Compare nums[mid] with nums[right]: 🔹If nums[mid] > nums[right] → minimum lies in the right half 🔹If nums[mid] < nums[right] → minimum lies in the left half (including mid) 🔹If equal → safely shrink the search space by decrementing right This handles the ambiguity caused by duplicates. ⏱️ Time Complexity: Average: O(log n) Worst case: O(n) (when many duplicates exist) 📦 Space Complexity: O(1) #100DaysOfCode #LeetCode #DSA #BinarySearch #Arrays #Java #ProblemSolving #InterviewPrep #LearningInPublic

🚀 Day 28 of #100DaysOfCode Solved 148. Sort List on LeetCode 🔗📊 🧠 Why this problem is interesting: Arrays are easy to sort, but linked lists don’t allow random access. That’s why Merge Sort becomes the perfect choice here. ⚙️ Approach (Merge Sort on Linked List): 🔹Use slow & fast pointers to find the middle 🔹Split the list into two halves 🔹Recursively sort both halves 🔹Merge two sorted linked lists ⏱️ Time Complexity: O(n log n) 📦 Space Complexity: O(log n) (recursive stack) #100DaysOfCode #LeetCode #DSA #LinkedList #MergeSort #Java #ProblemSolving #Consistency #LearningInPublic

🚀 Day 82 of #100DaysOfCode 🔍 Problem Solved: Find Minimum in Rotated Sorted Array Today I worked on a binary search based problem where we need to find the minimum element in a rotated sorted array in O(log n) time. 💡 Key Insight: If the array is already sorted (nums[low] <= nums[high]), then the first element is the minimum. Otherwise: If left half is sorted → minimum must be in right half If right half is unsorted → minimum lies there By carefully adjusting low and high, we narrow down the search space efficiently. 🧠 What I practiced: Modified Binary Search Identifying sorted halves Avoiding unnecessary comparisons #Java #DSA #BinarySearch #LeetCode #ProblemSolving #CodingJourney

🚀 Day 39 of #100DaysOfCode Solved 80. Remove Duplicates from Sorted Array II on LeetCode 🔢 🧠 Key Insight: The array is already sorted, and we need to ensure that each element appears at most twice, modifying the array in-place. ⚙️ Approach: 🔹Maintain a pointer i representing the position to place the next valid element 🔹Start iterating from index 2 🔹For each element nums[j], compare it with nums[i] 🔹If they are different, place the element at nums[i + 2] and move the pointer forward This ensures that no element appears more than twice while maintaining the sorted order. ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(1) #100DaysOfCode #LeetCode #DSA #Arrays #TwoPointers #Java #ProblemSolving #InterviewPrep #LearningInPublic

🚀 Day 34 of #100DaysOfCode Solved 852. Peak Index in a Mountain Array on LeetCode ⛰️📈 🧠 Key Insight: A mountain array strictly increases to a peak and then decreases. Instead of scanning the whole array, we can use Binary Search to efficiently find the peak. ⚙️ Approach: 🔹Use binary search with two pointers left and right 🔹Compare arr[mid] with arr[mid + 1] 🔹If arr[mid] > arr[mid + 1] → peak is on the left side (including mid) 🔹Otherwise → peak is on the right side 🔹Continue until left == right, which gives the peak index ⏱️ Time Complexity: O(log n) 📦 Space Complexity: O(1) #100DaysOfCode #LeetCode #DSA #BinarySearch #Arrays #Java #ProblemSolving #InterviewPrep #LearningInPublic

𝗬𝗼𝘂 𝗵𝗮𝘃𝗲 𝗯𝗲𝗲𝗻 𝘂𝘀𝗶𝗻𝗴 𝗕𝗶𝗻𝗮𝗿𝘆 𝗦𝗲𝗮𝗿𝗰𝗵 𝘄𝗿𝗼𝗻𝗴. It is not just for sorted arrays. The real definition — "Eliminate HALF the search space with each decision." That one shift in thinking unlocks 20+ LeetCode problems. Swipe to see the template, live code, and 7 problems you can now solve with one pattern. 👇 Save this. 🔖 #DSA #BinarySearch #Java #LeetCode #CodingInterview #DebugWithPurpose

LeetCode 1593 – Split a String Into the Max Number of Unique Substrings An ideal problem to understand Backtracking. The task is simple but tricky: Split a string into substrings such that all substrings are unique, and maximize the number of splits. 🚀 Approach (Backtracking) 1. Start from index i and try every possible substring s[i...j]. 2. If the substring is not already used (checked using a HashSet), we: add it to the set recursively explore the remaining string starting from j + 1 3. Each recursive call increases the current split count. 4. When we reach the end of the string (i >= length), we update the maximum number of unique splits. 5. After recursion, we remove the substring from the set (backtrack) so other possibilities can be explored. # Core Backtracking Pattern Choose → Explore → Undo Choose: Add substring to the set Explore: Recurse for the remaining string Undo: Remove substring to try other partitions Backtracking explores all possible partitions, but the HashSet ensures no substring repeats, giving the maximum valid split. Perfect example of how recursion + state tracking can systematically search the solution space. #LeetCode #Backtracking #Java #DSA #CodingInterview #Recursion

🚀 Day 50 / 100 | Median of Two Sorted Arrays Intuition: We are given two sorted arrays and need to find the median of the combined numbers. Since both arrays are already sorted, we can merge them in sorted order. Once we have the merged array, finding the median becomes simple. If the total number of elements is odd, the median is the middle element. If it's even, the median is the average of the two middle elements. Approach: Use two pointers to traverse both arrays. Compare the elements and insert the smaller one into a new array. Continue this process until all elements are merged. Finally, calculate the median based on the length of the merged array. Complexity: Time Complexity: O(n + m) Space Complexity: O(n + m) #100DaysOfCode #Java #DSA #LeetCode #ProblemSolving

Day 25 - Search Insert Position Technique Used: Binary Search Key Idea: Applied binary search to locate the target in a sorted array. If the target is not found, returned the index where it would be inserted while maintaining sorted order. The final start pointer represents the correct insertion position. Time Complexity: O(log n) #Day25 #LeetCode #Java #BinarySearch #DSA #Algorithms

🚀 Day 24 of #100DaysOfCode Solved 167. Two Sum II – Input Array Is Sorted on LeetCode 🔢👉👈 🧠 Key insight: Because the array is already sorted, we can avoid hash maps and use a two-pointer approach to find the target sum efficiently. ⚙️ Approach: 🔹Initialize two pointers at the start and end of the array 🔹Calculate the sum of values at both pointers 🔹If the sum is too large → move the right pointer left 🔹If the sum is too small → move the left pointer right 🔹Stop when the target is found ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(1) #100DaysOfCode #LeetCode #DSA #TwoPointers #Java #ProblemSolving #LearningInPublic #CodingJourney