Binary Search Deep Dive: LeetCode 34 Solution

🚨 If you're solving LeetCode but still not improving… read this. Most people just solve questions ❌ Top candidates understand patterns + solutions ✅ That’s the difference. --- 🔥 Complete LeetCode Solutions – FREE PDF This is not just questions… This is a full solution guide to master DSA step-by-step 💯 --- 📘 What’s inside: ✔ Linked List problems (cycle, reverse, merge, etc.) ✔ Trees & BST (traversals, depth, LCA, validation) ✔ Graphs (connected components, course schedule) ✔ Arrays & Strings (2 sum, 3 sum, sliding window) ✔ Recursion & Backtracking ✔ Dynamic Programming concepts ✔ Bit Manipulation & Maths ✔ Clean Python solutions for each problem --- 💡 From the document: You’ll find structured solutions starting from basics like Linked List Cycle to advanced problems like Binary Tree Maximum Path Sum, helping you build real problem-solving skills --- ⚠️ Hard truth: LeetCode alone won’t help… 👉 Understanding solutions WILL. --- 🎯 Best way to use this: 1. Try question yourself 2. Check solution 3. Understand logic deeply 4. Re-implement without seeing Repeat this → You’ll become dangerous in DSA 🔥 --- If this helps you: ❤️ Like 🔁 Repost 💬 Comment “LEETCODE” → I’ll share roadmap + more PDFs --- #leetcode #dsa #coding #interviewprep #developers #programming #python #placements #softwareengineer #learncoding

🚀 Day 86 of DSA Problem Solving 💡 Problem Solved: Next Greater Element I 🔍 Problem Idea: Given two arrays where nums1 is a subset of nums2, the task is to find the next greater element for each value in nums1 based on its position in nums2. 👉 The next greater element is the first element to the right that is larger than the current number. 🧠 Key Learning: Instead of using a brute-force approach (O(n²)), we optimize using a Monotonic Stack. ✔ Traverse nums2 from right to left ✔ Maintain a decreasing stack ✔ Pop all smaller elements → the top becomes the next greater ⚙️ Concepts Practiced: Monotonic Stack Stack + HashMap combination Efficient lookup strategy Right-to-left traversal ⏱️ Time Complexity: O(n + m) → Linear time using stack O(n) space for stack + hashmap 💭 Real Journey Behind the Solution: At first glance, it feels like a simple comparison problem 🤔 But brute force quickly becomes inefficient. The real breakthrough came when I realized: 👉 “We don’t need to check repeatedly — we can store useful answers while traversing once.” That’s where the monotonic stack pattern clicked 💡 A powerful technique I’m seeing again and again in DSA! 🔥 This problem strengthened my understanding of: Pattern recognition in stacks Writing optimal solutions under constraints 📌 Consistency is the real game. Small daily wins → Big long-term growth. #Day86 #DSA #LeetCode #CodingJourney #Java #Stack #MonotonicStack #ProblemSolving #Consistency #100DaysOfCode

DSA LeetCode Solutions Handbook – From Linked List to Trees Most people solve problems on LeetCode… But don’t organize their learning. That’s where real improvement gets missed. Found this structured handbook and it’s worth exploring . It covers key patterns across: 1) Linked List – pointers, cycles, reversal. 2) Trees – traversals, recursion, BSTs. 3) Graphs – BFS, DFS, shortest paths. 4) Heaps – Top K, priority queues. 5) Arrays & Strings – sliding window, two pointers. 6) Bit Manipulation – tricks that save time in interviews. 7) Maths – number theory, edge cases. 8) Matrix – grid-based problems & patterns. While building this, one thing became clear: - Solving problems is good, but organizing patterns is what truly improves speed and confidence. - This is something I’ll keep updating as I solve more problems. If you're preparing for coding interviews, structured notes like this can really help. Do you maintain your own notes while solving DSA problems? #DSA #LeetCode #CodingInterview #Python #ProblemSolving #SoftwareEngineering #TechLearning #PythonDeveloper

Most people quit DSA before this point… I didn’t. Just crossed 100 problems solved on LeetCode 💻 Not going to sugarcoat it — this wasn’t smooth. Got stuck on “easy” problems, revisited basics multiple times, and had days where nothing clicked. But here’s what changed: • Focused on consistency over motivation • Repeated problems until patterns made sense • Started thinking in approaches, not just solutions Current progress: → 100+ problems solved → Moving from Easy → Medium → Building stronger fundamentals in Data Structures & Algorithms (DSA) → Recently earned a Pandas badge 🐼 Still a long way to go — this is just the base. Next target: better speed, deeper problem-solving, and more Medium/Hard questions. If you’re starting out or stuck — keep going. It compounds. What was the toughest phase in your DSA journey? LeetCode profile link -- https://lnkd.in/gU9-x_qz #LeetCode #DSA #ProblemSolving #CodingJourney #Python #SoftwareEngineering #TechCareers #100DaysOfCode #LearnInPublic #Consistency

🚀 DSA Journey — Day 17: Mastering Binary Search (LeetCode 704) Today I worked on one of the most fundamental and powerful algorithms in DSA — Binary Search. 🔍 Problem Understanding Given a sorted array, we need to efficiently find the index of a target element. If it exists, return its index; otherwise, return -1. 💡 Brute Force Approach Traverse the array linearly Compare each element with target Time Complexity: O(n) ⚠️ Not optimal for large datasets ⚡ Optimized Approach — Binary Search Since the array is sorted, we can eliminate half of the search space in every step. 👉 Steps: Initialize start = 0, end = n-1 Find middle: mid = (start + end) / 2 Compare: If nums[mid] == target → return index If target < mid → move left (end = mid - 1) If target > mid → move right (start = mid + 1) Repeat until found or search space ends 🧠 Example Walkthrough Array: [-1,0,3,5,9,12], Target: 9 mid = 2 → value = 3 → move right mid = 4 → value = 9 → ✅ Found ⏱️ Complexity Analysis Time Complexity: O(log n) Space Complexity: O(1) 🎯 Key Learning Binary Search is not just a problem — it's a pattern. Understanding this deeply will help in: Searching problems Optimization problems Many advanced DSA questions 🙏 Gratitude Grateful for the consistency and learning mindset every day 🙌 📈 Consistency is the real game changer. One problem a day = big results. #DSA #BinarySearch #LeetCode #CodingJourney #Java #ProblemSolving #Consistency #Learning #TechJourney #100DaysOfCode

Day 37 | Binary Search Today I learned and practiced Binary Search as part of my journey with 10000 Coders. Binary Search is an efficient searching algorithm that works on sorted arrays. Instead of checking each element one by one, it repeatedly divides the search space into half. How it works: Find the middle element Compare it with the target value • If equal → return index • If target is smaller → search in left half If target is greater → search in right half This process continues until the element is found or the search space becomes empty. Key Learnings: Time Complexity: O(log n) Importance of sorted data Efficient compared to linear search • Use of low, high, and mid pointers Practicing Binary Search helped me improve my understanding of: Searching algorithms Divide and conquer approach Writing efficient code Step by step improving my DSA and problem-solving skills 6 #Day37#Python #BinarySearch #DSA #ProblemSolving #Algorithms #10000Coders #PythonJourney #FullStackJourney #SravanKumarSir

🚀 Day 22 of My DSA Journey Today I solved an important Binary Search problem: 👉 Find First and Last Position of Element in Sorted Array 🧩 Problem Understanding Given a sorted array and a target value, we need to find the starting and ending position of the target element. If the target is not present, return [-1, -1]. 🔍 Approach I Used (Binary Search x2) Instead of linear search, I used Binary Search twice: ✔️ First Binary Search → to find the first occurrence ✔️ Second Binary Search → to find the last occurrence 💡 Trick: Even after finding the target, we don’t stop — we continue searching on left/right side to get the boundary indices. ⚡ Why this approach? Because the array is sorted → Binary Search reduces time complexity drastically. ⏱ Time Complexity: O(log n) 📦 Space Complexity: O(1) 🔥 Key Learning Binary Search is not just for finding elements — it can also be used to find boundaries, ranges, and conditions. Consistency is the real game changer 💪 One problem at a time! #DSA #BinarySearch #LeetCode #Java #ProblemSolving #100DaysOfCode #CodingJourney #LearningInPublic

🚀 Solved “Search Insert Position” using Binary Search on LeetCode! Today I worked on an interesting problem where the goal is to find the index of a target element in a sorted array. If the target is not present, we return the index where it should be inserted to maintain sorted order. 🔍 Approach: Instead of using a linear search (O(n)), I implemented an efficient Binary Search (O(log n)) approach. 💡 Key Learning: One important detail is calculating the middle index safely: mid = low + (high - low) / 2 This avoids potential integer overflow compared to (low + high) / 2 and ensures correct behavior even for large inputs. ⚙️ Logic: If target == arr[mid] → return mid If target > arr[mid] → search right half Else → search left half If not found → return ‘low’ as the correct insert position 📈 Result: Achieved 100% performance on LeetCode 🚀 This problem reinforced my understanding of: ✔ Binary Search fundamentals ✔ Edge case handling ✔ Writing optimized and safe code Looking forward to solving more problems and improving problem-solving skills! #LeetCode #BinarySearch #Java #DataStructures #Coding #ProblemSolving

🚀 Mastering Efficiency: Solving the Maximum Subarray Problem | LeetCode Just cleared the Maximum Subarray challenge! This problem is a perfect example of how a simple shift in logic—moving from brute force to a greedy approach—can drastically optimize performance. 💡 The Problem: Given an integer array nums, find the subarray with the largest sum and return its sum. ⚡ My Approach (Kadane's Algorithm): Instead of calculating every possible subarray sum, I used a greedy strategy to decide at each step whether to keep the current running sum or "start fresh" from the current element. 👉 The Logic: Initialize: Start with max_sum as the first element and a curr_sum of 0. The "Fresh Start" Rule: As I iterate, if curr_sum becomes negative, it’s a burden. I reset it to 0 because any subarray starting with a negative sum will only decrease the potential total. Accumulate: Add the current number to curr_sum. Update Global Max: Compare curr_sum with max_sum and store the higher value. 🔥 Complexity Analysis: ⏱ Time Complexity: $O(n)$ – A single, clean pass through the array. 📦 Space Complexity: $O(1)$ – Constant space; no extra data structures needed. 🏆 The Result: ✔️ Accepted: Passed all 210 test cases. ✔️ Performance: 28 ms runtime, beating 79.77% of Python3 submissions! 📌 Key Learning: Kadane’s Algorithm is a masterclass in dynamic programming/greedy logic. It teaches you to discard "baggage" (negative sums) and focus only on what contributes to the optimal goal. 💻 Tech Stack: #Python | #Algorithms | #DataStructures #leetcode #dsa #coding #programming #100DaysOfCode #softwareengineering #kadanesalgorithm #optimization #techcommunity

🚀 Solved Two Sum Problem with Optimal Approach | LeetCode Today I solved the classic Two Sum problem and focused on writing an efficient solution rather than just making it work. 💡 Problem: Given an array of integers, return indices of two numbers such that they add up to a target. ⚡ Approach: Instead of using the brute force method, I used a HashMap (dictionary) to store elements and their indices. 👉 Logic: Traverse the array once For each element, calculate: difference = target - current value Check if difference already exists in the HashMap If yes → return indices instantly 🔥 Time & Space Complexity: ⏱ Time Complexity: O(n) 📦 Space Complexity: O(n) 🚀 Optimization: Improved from brute force O(n²) → O(n) using HashMap lookup 🏆 Result: ✔️ Accepted (All test cases passed) ✔️ Runtime: 0 ms (Beats 100%) 📌 Key Learnings: HashMap enables constant time lookup Thinking in terms of complement simplifies problems Optimization is key in coding interviews 💻 Tech Stack: Python | Data Structures & Algorithms 📈 Consistency + Practice = Growth 🚀 #leetcode #dsa #python #algorithms #coding #programming #softwareengineering #100DaysOfCode #tech