3Sum Problem Solution on LeetCode

🚀 50 Important Coding Questions – Question 36/50 🔹 Next Greater Element I | LeetCode A classic Monotonic Stack problem used frequently in coding interviews. 📌 Problem Statement You are given two arrays nums1 and nums2. For each element in nums1, find the next greater element in nums2. The next greater element of x is the first element to the right of x in nums2 that is greater than x. If it does not exist → return -1. Example: Input nums1 = [4,1,2] nums2 = [1,3,4,2] Output [-1,3,-1] 💡 Approach We use a Monotonic Decreasing Stack. Steps: 1️⃣ Traverse nums2 from right to left 2️⃣ Remove all elements from stack ≤ current element 3️⃣ The stack top becomes the next greater element 4️⃣ Store results in a map/array 5️⃣ Finally answer queries for nums1 This avoids checking every element repeatedly. ⏱ Time Complexity: O(n + m) 📦 Space Complexity: O(n) 📌 LeetCode Result: ✔ Accepted ⚡ Runtime: 0 ms 🧠 Concepts Strengthened ✔ Monotonic Stack ✔ Stack-based optimization ✔ Array traversal techniques ✔ Precomputation for faster queries 📍 Question 36 of 50 in my “50 Important Coding Questions” series. Every problem solved improves algorithmic thinking step by step 💯 👉 Question 37 coming next! #DSA #LeetCode #Stack #MonotonicStack #CodingInterview #ProblemSolving #CPlusPlus #TechJourney

🚀 50 Important Coding Questions – Question 20/50 🔹 Longest Consecutive Sequence | LeetCode (Medium) A powerful Hash Set problem that teaches how to detect sequences efficiently without sorting 👇 📌 Problem Statement Given an unsorted array of integers nums, return the length of the longest consecutive elements sequence. 👉 Must solve in O(n) time. 💡 Optimized Approach (Hash Set) 1️⃣ Store all numbers in a set 2️⃣ Only start counting when num-1 is NOT in set 3️⃣ Keep checking num+1 until sequence ends 4️⃣ Track maximum length ⏱ Time Complexity: O(n) 📦 Space Complexity: O(n) ✅ Why this problem is important? ✔ Strengthens Hash Set concepts ✔ Teaches sequence detection logic ✔ Very common in FAANG interviews ✔ Shows how to reduce sorting to linear time 📌 LeetCode Result: ✔ Accepted ⚡ Efficient O(n) solution 🔔 This is Question 20 of my “50 Important Coding Questions” series. Almost halfway — keep the grind going 💪 👉 Question 21 coming soon… #DSA #LeetCode #LongestConsecutiveSequence #HashSet #Arrays #CPlusPlus #CodingInterview #ProblemSolving #50ImportantQuestions

🚀 50 Important Coding Questions – Question 17/50 🔹 Longest Substring Without Repeating Characters | LeetCode (Medium) A core Sliding Window problem that every coding interview candidate must master 👇 📌 Problem Statement Given a string s, find the length of the longest substring without repeating characters. 💡 Optimized Approach (Sliding Window + Hash Set / Map) 1️⃣ Use two pointers → left and right 2️⃣ Expand window while characters are unique 3️⃣ If duplicate found → move left until unique 4️⃣ Track maximum length ⏱ Time Complexity: O(n) 📦 Space Complexity: O(k) (character set size) ✅ Why this problem is important? ✔ Teaches Sliding Window mastery ✔ Improves substring handling logic ✔ Very common in FAANG interviews ✔ Foundation for many string problems 📌 LeetCode Result: ✔ Accepted ⚡ Efficient sliding window solution 🔔 This is Question 17 of my “50 Important Coding Questions” series. Keep coding daily — results will follow 💪 👉 Question 18 coming soon… #DSA #LeetCode #SlidingWindow #LongestSubstring #Strings #CPlusPlus #CodingInterview #ProblemSolving #50ImportantQuestions

🚀 50 Important Coding Questions – Question 27/50 🔹 Sort List | LeetCode (Medium) A very important Linked List problem that teaches sorting using Merge Sort 📚 📌 Problem Statement Given the head of a linked list, sort it in ascending order. 💡 Optimal Approach – Merge Sort on Linked List Why Merge Sort? Because Linked Lists don’t support random access like arrays. 👉 Steps 1️⃣ Find middle of list using slow & fast pointers 2️⃣ Split list into two halves 3️⃣ Recursively sort both halves 4️⃣ Merge sorted lists 🧠 Why this problem is important? ✔ Teaches Merge Sort logic ✔ Important interview question ✔ Helps in advanced Linked List problems ✔ Improves recursion understanding ⏱ Time Complexity: O(n log n) 📦 Space Complexity: O(log n) (recursion stack) 👉 My approach used array conversion + sorting, but Merge Sort is the optimal in interviews. 📌 LeetCode Result: ✔ Accepted ⚡ Efficient solution 🔔 This is Question 27 of my “50 Important Coding Questions” series. Linked List concepts getting stronger 💪 👉 Question 28 coming soon… #DSA #LeetCode #LinkedList #MergeSort #CodingInterview #CPlusPlus #ProblemSolving #50ImportantQuestions

🥷 Day 301 of My 365 LeetCode Challenge is done! 💥 Kicked off strong, solved my problem, and the coding vibe is awesome! 🧠 📌 LeetCode 1758 – Minimum Changes To Make Alternating Binary String Sometimes the simplest problems offer the best opportunity to strengthen your fundamentals. Today’s challenge focused on string manipulation and pattern recognition — two concepts that frequently appear in coding interviews. 🧠✨ 🔍 Problem Overview We’re given a binary string consisting only of 0s and 1s. The goal is to determine the minimum number of changes required to make the string alternating. An alternating string means: ✔ No two adjacent characters are the same ✔ Valid patterns would look like "010101..." or "101010..." So essentially, the string must follow one of two possible patterns. ⚙️ Key Idea Behind the Solution Instead of trying complex transformations, we can simply compare the string against the two valid alternating patterns: 1️⃣ Pattern starting with 0 → 010101... 2️⃣ Pattern starting with 1 → 101010... By counting how many positions differ from each pattern, we get the number of changes required for each scenario. The final answer is simply the minimum of the two counts. 💡 Why This Problem Matters Even though it’s categorized as an easy problem, it reinforces several important skills: • Pattern-based thinking • Efficient string traversal • Writing concise and optimized logic • Recognizing that sometimes brute-force comparison can still be optimal 🧠 Key Takeaway Not every coding challenge requires complex algorithms. Many problems reward clear observation and structured thinking. Mastering these fundamentals builds a strong foundation for tackling more advanced algorithmic challenges. On to the next challenge! 💪🔥 #LeetCode #DSA #Strings #Algorithms #ProblemSolving 🧠 #CodingJourney #InterviewPrep #CPlusPlus 🚀

🚀 50 Important Coding Questions – Question 18/50 🔹 Group Anagrams | LeetCode (Medium) A classic Hashing + Sorting problem that teaches how to group data efficiently 👇 📌 Problem Statement Given an array of strings, group the anagrams together. Two strings are anagrams if they contain the same characters with the same frequency. 💡 Optimized Approach 1️⃣ Sort each string → get a canonical form 2️⃣ Use a Hash Map → key = sorted string 3️⃣ Store original strings in the same group Example: 👉 "eat", "tea", "ate" → all become "aet" → same group ⏱ Time Complexity: O(n * k log k) (k = average string length) 📦 Space Complexity: O(nk) ✅ Why this problem is important? ✔ Strengthens Hash Map concepts ✔ Teaches grouping techniques ✔ Common in FAANG interviews ✔ Foundation for advanced string problems 📌 LeetCode Result: ✔ Accepted ⚡ Efficient hashing solution 🔔 This is Question 18 of my “50 Important Coding Questions” series. You’re already over 1/3 of the way — keep going 💪 👉 Question 19 coming soon… #DSA #LeetCode #GroupAnagrams #Hashing #Sorting #Strings #CPlusPlus #CodingInterview #ProblemSolving #50ImportantQuestions

🚀 50 Important Coding Questions – Question 37/50 🔹 Next Greater Element II | LeetCode An extension of the Next Greater Element problem, but with a circular array twist. 📌 Problem Statement Given a circular integer array, return the next greater number for every element. The next greater number of a number x is the first greater number to its right in the array. Since the array is circular, the search may continue from the beginning. If no greater element exists → return -1. Example: Input nums = [1,2,1] Output [2,-1,2] 💡 Approach We use a Monotonic Decreasing Stack. Key idea: 1️⃣ Traverse the array twice (2n) to simulate circular behavior 2️⃣ Use a stack to store indices 3️⃣ Remove elements from stack while they are ≤ current element 4️⃣ The stack top becomes the next greater element 5️⃣ Store results in the answer array This avoids checking the array repeatedly. ⏱ Time Complexity: O(n) 📦 Space Complexity: O(n) 📌 LeetCode Result: ✔ Accepted ⚡ Efficient stack-based implementation 🧠 Concepts Strengthened ✔ Monotonic Stack ✔ Circular array handling ✔ Stack optimization ✔ Efficient traversal techniques 📍 Question 37 of 50 in my “50 Important Coding Questions” series. Step by step building stronger DSA fundamentals 💯 👉 Question 38 coming next! #DSA #LeetCode #Stack #MonotonicStack #CodingInterview #ProblemSolving #CPlusPlus #TechJourney

🚀 50 Important Coding Questions – Question 12/50 🔹 Valid Anagram | LeetCode (Easy) A simple yet powerful Hashing problem that builds strong string manipulation fundamentals 👇 📌 Problem Statement Given two strings s and t, return true if t is an anagram of s, and false otherwise. 👉 An anagram contains the same characters with the same frequencies. 💡 Optimized Approach (Hash Map / Frequency Count) 1️⃣ If lengths are different → return false 2️⃣ Count character frequencies of first string 3️⃣ Decrease counts using second string 4️⃣ If all counts match → valid anagram ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) (since character set is limited) ✅ Why this problem is important? ✔ Introduces frequency counting technique ✔ Foundation for many string & hashing problems ✔ Commonly asked in coding interviews ✔ Builds base for grouping anagrams 📌 LeetCode Result: ✔ Accepted ⚡ Runtime: 0 ms (Beats 100%) 📊 Memory Efficient 🔔 This is Question 12 of my “50 Important Coding Questions” series. Slow progress is still progress 💪 👉 Question 13 coming soon… #DSA #LeetCode #ValidAnagram #Hashing #Strings #CPlusPlus #CodingInterview #ProblemSolving #50ImportantQuestions

🚀 50 Important Coding Questions – Question 15/50 🔹 Reverse Vowels of a String | LeetCode (Easy) A simple but effective Two Pointers string problem that improves string traversal logic 👇 📌 Problem Statement Given a string s, reverse only all the vowels in the string and return it. 👉 Vowels = a, e, i, o, u (case-insensitive) 💡 Optimized Approach (Two Pointers) 1️⃣ Use two pointers → start & end 2️⃣ Move pointers until vowels are found 3️⃣ Swap vowels 4️⃣ Continue until pointers meet ⏱ Time Complexity: O(n) 📦 Space Complexity: O(1) ✅ Why this problem is important? ✔ Strengthens Two Pointer technique ✔ Improves string manipulation skills ✔ Common warm-up interview question ✔ Builds intuition for palindrome & substring problems 📌 LeetCode Result: ✔ Accepted ⚡ Runtime: 0 ms (Beats 100%) 📊 Memory Efficient 🔔 This is Question 15 of my “50 Important Coding Questions” series. Small steps every day = Big results 💪 👉 Question 16 coming soon… #DSA #LeetCode #ReverseVowels #TwoPointers #Strings #CPlusPlus #CodingInterview #ProblemSolving #50ImportantQuestions

The secret isn't solving 500 problems randomly. It's solving the right problems with the right progression. . . 150 days of consistency can build the kind of thinking interviews actually test. 1. Arrays & Strings Most coding rounds start here. Practice problems on prefix sum, two pointers, sliding window, and subarrays. 2. Hashing HashMaps and HashSets help reduce time complexity drastically Be comfortable solving frequency and lookup based problems. 3. Recursion & Backtracking Important for problems like subsets, permutations, combinations, and N-Queens. 4. Linked List Reverse linked list, cycle detection, merging lists, and pointer manipulation. 5. Stack & Queue Learn monotonic stack, next greater element, and basic queue operations. 6. Trees & Binary Trees Tree traversals (inorder, preorder, postorder), height, diameter, LCA, and BFS. 7. Binary Search Not just on arrays. Also practice binary search on answer problems. 8. Dynamic Programming Start with memoization, then tabulation. Classic problems: knapsack, LIS, DP on grids. 9. Graphs BFS, DFS, shortest path, topological sort, union find. 10. Time & Space Complexity Always analyze the complexity of your solution before finishing. Connect Swadesh Kumar for more such content Comment 'MAANG' & I'll DM you the PDF directly 150 focused probelms> 300+ random question practice. If you're preparing seriously for MAANG, this roadmap can change your trajectory.