Closest Pair Sum in an unsorted Array | 2-pointer algorithm | DSA скачать в хорошем качестве

Closest Pair Sum in an unsorted Array | 2-pointer algorithm | DSA

Question link: https://www.geeksforgeeks.org/problem... Given an array arr[] and an integer target. You have to find a pair in an array whose sum is closest to target. Note: Return the pair in sorted order and if there are multiple such pairs return the pair with maximum absolute difference. If no such pair exists return an empty array. Given an integer array and a target value, the task is to find a pair of elements whose sum is closest to the given target. If multiple pairs have the same minimum absolute difference from the target: Solution 1 (Tuple + PriorityQueue) selects the pair having the maximum difference between the two elements. Steps: Sort the array. Use two pointers (i from start, j from end). For each pair (a[i], a[j]), calculate: Absolute difference from the target Store (difference, a[i], a[j]) inside a PriorityQueue. The PriorityQueue keeps pairs ordered by minimum difference. If multiple pairs have the same closest sum: Choose the pair with the maximum |a - b|. Return the selected pair. Why use this approach? When the problem requires extra rules or tie-breaking logic. Easy to extend for more conditions. Time Complexity: Sorting: O(n log n) Two-pointer scan: O(n) Overall: O(n log n) Space Complexity: O(n) (PriorityQueue) Solution 2 (Optimized Two Pointer) returns any one closest pair without extra tie-breaking. Steps: Sort the array. Use two pointers. Track the minimum absolute difference seen so far. Update the answer whenever a better pair is found. If exact target sum is found, return immediately. Time Complexity: O(n log n) Space Complexity: O(1) Whether you're preparing for FAANG, product-based companies, coding rounds, or want to strengthen your fundamentals, this channel brings you the best structured DSA explanations with zero confusion. Step-by-step logic Mathematical reasoning Multiple approaches (Naive → Optimized) Time & Space complexity Real interview insights Clean and easy-to-understand code Two Pointers Array Sorting Priority Queue Greedy Algorithm Absolute Difference Closest Pair Sum Interview Preparation Data Structures (Arrays, Strings, Stacks, Queues, Trees, Graphs, LinkedList, Hashing, Heaps) Algorithms (Sorting, Searching, Binary Search, Greedy, DP, Recursion, Backtracking, Graph Algorithms) Math Logic for coding (Number Theory, Combinatorics, Modular Arithmetic) Coding interview questions with full proof and code Competitive programming tricks and optimizations LeetCode, GFG, Codeforces, HackerRank problem explanations Pattern printing & logic building Time complexity mastery 👍 Like the video 💬 Comment your doubts 🔔 Subscribe for daily coding content 📤 Share with your coding friends Your support motivates me to create more high-quality content. Tags: #DSA #Coding #CompetitiveProgramming #Programmer #DataStructures #Algorithms #LeetCode #GeeksForGeeks #CodingInterview #SoftwareEngineer #CodeWithMe #MathInCode #Optimization #CP #Java #Python #C++ #LogicBuilding #ProblemSolving #TechInterview #ProgrammingTutorial #ComputerScience #CodingChallenge #DSATutorial #CodingForBeginners #Code #BinarySearch #Recursion #DynamicProgramming coding, dsa, data structures and algorithms, competitive programming, problem solving, coding interview, interview preparation, geeks for geeks, leetcode solutions, codeforces, hackerRank, programming tutorial, algorithm explanation, data structures tutorial, dynamic programming, recursion, binary search, greedy algorithms, coding tricks, optimize code, coding shortcuts, math logic in code, beginners coding, java coding, python coding, cpp coding, technical interview, placements preparation, coding questions, solved problems, coding solution, youtube coding channel, logic building, number theory, mathematics for coding, time complexity, space complexity