LeetCode Discipline: Remove Duplicates from Sorted Array

🚀 Day 24/60 — LeetCode Discipline Problem Solved: Symmetric Tree (Revision) Difficulty: Easy Today’s practice focused on revisiting a classic binary tree problem — checking whether a tree is symmetric around its center. The key idea is to treat the left and right subtrees as mirror images and recursively compare their corresponding nodes. By verifying both structure and node values simultaneously, the algorithm determines whether the tree maintains perfect symmetry. Problems like this highlight how recursion naturally fits tree-based structures and helps simplify complex comparisons. 💡 Focus Areas: • Strengthened recursive tree traversal • Practiced mirror comparison of subtrees • Improved understanding of binary tree symmetry • Reinforced recursive problem-solving patterns • Focused on writing clean and structured logic ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance) on submission. Consistent practice across arrays, strings, stacks, bit manipulation, and now tree structures continues to deepen my overall algorithmic intuition. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #BinaryTree #Recursion #Algorithms #DataStructures #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers #Java

🚀 Day 26/60 — LeetCode Discipline Problem Solved: Remove Element (Revision) Difficulty: Easy Today’s practice focused on revisiting a fundamental array problem involving in-place modification. The task was to remove all occurrences of a given value without using extra space, while efficiently maintaining the remaining elements. The solution leverages a simple yet powerful approach of iterating through the array and overwriting unwanted elements. Problems like this reinforce the importance of space optimization and clean in-place operations, which are often crucial in real-world scenarios. 💡 Focus Areas: • Strengthened in-place array manipulation • Practiced efficient element filtering • Reinforced two-pointer style iteration • Improved understanding of space optimization • Focused on writing concise and readable code ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance) on submission. Simple problems, when practiced with discipline, continue to sharpen the core of problem-solving ability. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #Arrays #TwoPointers #Algorithms #DataStructures #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers #Java

🚀 Day 25/60 — LeetCode Discipline Problem Solved: Search Insert Position (Revision) Difficulty: Easy Today’s practice focused on revisiting one of the most fundamental algorithms in computer science — Binary Search. The task was to efficiently determine the position of a target element in a sorted array, or identify the correct index where it should be inserted while maintaining order. This problem reinforces how dividing the search space in half at each step leads to highly efficient solutions with logarithmic time complexity. 💡 Focus Areas: • Strengthened binary search fundamentals • Practiced boundary condition handling • Improved mid-index calculation logic • Reinforced logarithmic-time problem solving • Focused on writing clean and precise code ⚡ Performance Highlight: Achieved 0 ms runtime (100% performance) on submission. Revisiting foundational algorithms like binary search continues to sharpen problem-solving precision and efficiency. #LeetCode #60DaysOfCode #100DaysOfCode #DSA #BinarySearch #Algorithms #DataStructures #ProblemSolving #CodingJourney #SoftwareEngineering #Programming #Developers #TechCareers #Java

Day 38 of My DSA Journey Today I solved LeetCode 152 – Maximum Product Subarray on LeetCode. 📌 Problem Given an integer array nums, find the contiguous subarray that has the largest product, and return that product. Example: Input: [2,3,-2,4] Output: 6 → subarray [2,3] 🧠 Approach – Dynamic Programming (Tracking Max & Min) This problem is tricky because of negative numbers. Key Idea: • At each index, we maintain: maxProduct → maximum product ending at current index minProduct → minimum product ending at current index 👉 Why min? Because a negative number can turn a small (negative) product into a large positive one. Steps I followed: • Initialize maxProduct, minProduct, and ans with the first element • Traverse the array from left to right • If the current number is negative → swap max and min • Update: maxProduct = max(current, current × maxProduct) minProduct = min(current, current × minProduct) • Update the final answer using maxProduct ⏱ Time Complexity: O(n) — Single pass through the array 📦 Space Complexity: O(1) — No extra space used 💡 Key Learnings ✔ Handling negative numbers in product problems ✔ Using dynamic programming with state tracking ✔ Understanding why tracking both max & min is important This is one of those problems that looks simple but tests deep understanding of edge cases 🚀 Consistency continues — leveling up every day 💪 #100DaysOfCode #DSA #DynamicProgramming #Arrays #LeetCode #Java #ProblemSolving #CodingJourney #DeveloperJourney #Programming #SoftwareEngineering

🚀 Day 535 of #750DaysOfCode 🚀 ✅ Solved: Count Submatrices with Top-Left Element and Sum ≤ k (LeetCode 3070) Today’s problem was a great example of using 2D Prefix Sum to optimize matrix queries. Instead of checking every possible submatrix, we can observe that the question only allows submatrices that include the top-left element (0,0). This means every valid submatrix is just a prefix rectangle, so we can compute the sum efficiently using prefix sums. 💡 Key Learning: Used 2D Prefix Sum technique Reduced brute force complexity to O(m × n) Learned how to handle matrix range sum problems efficiently 📌 Approach: Build prefix sum for each cell Check if sum from (0,0) to (i,j) ≤ k Count valid submatrices This problem improved my understanding of: ✔️ Prefix Sum ✔️ Matrix DP patterns ✔️ Optimization from brute force to efficient solution Consistency continues 🔥 On to Day 536 tomorrow. #leetcode #java #datastructures #algorithms #codingchallenge #prefixsum #matrix #programming #softwareengineering #750daysofcode

🚀  Day 29 LeetCode Problem Solved: Longest Consecutive Sequence (128) Today I solved an interesting Data Structures & Algorithms problem on LeetCode. 💻 🔹 Problem: Given an unsorted array of integers, find the length of the longest consecutive elements sequence in O(n) time complexity. 🔹 Example: Input: [100, 4, 200, 1, 3, 2] Output: 4 👉 The longest consecutive sequence is [1,2,3,4]. 🔹 Approach: Instead of sorting the array (which takes O(n log n)), I used a HashSet to achieve O(n) time complexity. ✔ Store all numbers in a HashSet ✔ Identify the start of a sequence (num - 1 not present in the set) ✔ Expand the sequence forward (num + 1, num + 2...) ✔ Track the maximum length 🔹 Complexity: ⏱ Time Complexity: O(n) 📦 Space Complexity: O(n) 💡 Key Learning: Using HashSet efficiently can help optimize problems that involve searching and sequence detection. Excited to keep learning and improving problem-solving skills! 🚀 #leetcode #coding #java #datastructures #algorithms #softwaredeveloper #programming #codingjourney

Day 68 on LeetCode Guess Number Higher or Lower 🎯✅ Today’s problem reinforced the power of Binary Search on an answer space. 🔹 Approach Used in My Solution The goal was to identify a hidden number using the provided guess() API. Key idea in the solution: • Apply binary search between 1 and n • Pick mid and call guess(mid) • Based on the response:   – 0 → correct number found   – -1 → guessed number is too high → move left   – 1 → guessed number is too low → move right • Continue narrowing the search space until the number is found This is a perfect example of searching efficiently using feedback. ⚡ Complexity: • Time Complexity: O(log n) • Space Complexity: O(1) 💡 Key Takeaways: • Strengthened understanding of binary search with external APIs • Learned how to adjust search space based on feedback • Reinforced the concept of searching on answer space 🔥 Another solid step in mastering binary search patterns! #LeetCode #DSA #Algorithms #DataStructures #BinarySearch #DivideAndConquer #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #BuildInPublic #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Day 20 of my #30DayCodeChallenge: Efficiency through Pruning! Today's challenge was Word Search II-a complex puzzle that tests how you manage massive search spaces. The Logic: 1. Trie Integration: I stored the dictionary in a Trie to check prefixes in O(1) time. 2. DFS & Backtracking: Explored the grid cell by cell, but with a twist... 3. Intelligent Pruning: If a path doesn't match a Trie prefix, the search stops immediately. This turns an exponential problem into something much more manageable. Coding isn't just about finding the answer; it's about finding it before your timer runs out! #Java #DataStructures #Backtracking #Trie #Algorithms #CleanCode #150DaysOfCode

Day 61 on LeetCode — Contiguous Array (Find Max Length of Equal 0s and 1s) ⚖️✅ This problem is a classic application of prefix sum + hashmap technique. 🔹 Idea Behind the Solution The key trick is to convert the problem into a prefix sum problem: • Treat 0 as -1 and 1 as +1 • Maintain a running sum (sum) • If the same sum appears again at two indices, it means the subarray between them has equal number of 0s and 1s 🔹 How the HashMap Helps • Store the first occurrence of each prefix sum • If a prefix sum repeats at index i and was previously seen at index j, then:   i - j gives a balanced subarray length 🔹 Initialization Trick • seen{{0, -1}} ensures that subarrays starting from index 0 are correctly handled ⚡ Complexity: • Time: O(n) • Space: O(n) 💡 Key Takeaways: • Converting binary problems into prefix sum transformations simplifies logic • Hashmaps are powerful for tracking previous states of cumulative sums • Recognizing patterns where equal values → zero sum subarray is very useful 🔥 This is a very important pattern for array + hashmap + prefix sum problems! #LeetCode #DSA #Algorithms #DataStructures #PrefixSum #HashMap #Arrays #ProblemSolving #Coding #Programming #Cpp #STL #SoftwareEngineering #ComputerScience #CodingPractice #DeveloperLife #TechJourney #CodingDaily #100DaysOfCode #AlgorithmPractice #CodingSkills #Developers #TechCommunity #SoftwareDeveloper #EngineeringJourney

Day 78 - LeetCode Journey Solved LeetCode 142: Linked List Cycle II (Medium) today — a powerful extension of the cycle detection problem. Earlier, we learned how to detect if a cycle exists. Now the challenge is to find the exact node where the cycle begins. 💡 Core Idea (Floyd’s Algorithm Extended): 1) Use slow & fast pointers to detect a cycle 2) Once they meet, reset one pointer to the head 3) Move both pointers one step at a time 4) The point where they meet again = start of the cycle 🤯 Why it works? Because of the mathematical relationship between distances traveled inside the cycle — both pointers align perfectly at the cycle entry point. ⚡ Key Learning Points: • Advanced use of two-pointer technique • Understanding the mathematics behind cycle detection • Solving without modifying the list • Maintaining O(n) time and O(1) space This is not just coding — this is algorithmic thinking at a deeper level. Also, this pattern connects with: -> Find duplicate number (cycle in array) -> Happy Number problem -> Loop detection in graphs ✅ Stronger conceptual clarity ✅ Better problem-solving depth ✅ Confidence with tricky pointer problems From detecting a cycle to pinpointing its start — that’s real progress 🚀 #LeetCode #DSA #Java #LinkedList #TwoPointers #Algorithms #ProblemSolving #CodingJourney #Consistency #100DaysOfCode #InterviewPreparation #DeveloperGrowth #KeepCoding