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Count Inversions in an array | Set 1 (Using Merge Sort), Divide and Conquer | Set 2 (Closest Pair of Points), Divide and Conquer | Set 5 (Strassen’s Matrix Multiplication), K’th Smallest/Largest Element in Unsorted Array | Set 2 (Expected Linear Time), Greedy Algorithms | Set 1 (Activity Selection Problem), Job Sequencing Problem | Set 1 (Greedy Algorithm), Greedy Algorithms | Set 3 (Huffman Coding), Greedy Algorithms | Set 4 (Efficient Huffman Coding for Sorted Input), Greedy Algorithms | Set 2 (Kruskal’s Minimum Spanning Tree Algorithm), Greedy Algorithms | Set 5 (Prim’s Minimum Spanning Tree (MST)), Greedy Algorithms | Set 6 (Prim’s MST for Adjacency List Representation), Applications of Minimum Spanning Tree Problem, Greedy Algorithms | Set 7 (Dijkstra’s shortest path algorithm), Greedy Algorithms | Set 8 (Dijkstra’s Algorithm for Adjacency List Representation), Greedy Algorithm to find Minimum number of Coins, Breadth First Traversal or BFS for a Graph, Disjoint Set (Or Union-Find) | Set 1 (Detect Cycle in an Undirected Graph), Some interesting shortest path questions | Set 1, Shortest path with exactly k edges in a directed and weighted graph, Articulation Points (or Cut Vertices) in a Graph, Check if a graph is strongly connected | Set 1 (Kosaraju using DFS), Graph Algorithms | DFS and BFS | Minimum Spanning Tree | Shortest Paths | Connectivity, Dynamic Programming | Set 1 (Overlapping Subproblems Property), Dynamic Programming | Set 2 (Optimal Substructure Property), Dynamic Programming | Set 4 (Longest Common Subsequence), Dynamic Programming | Set 8 (Matrix Chain Multiplication), Dynamic Programming | Set 10 ( 0-1 Knapsack Problem), Dynamic Programming | Set 6 (Min Cost Path), Dynamic Programming | Set 25 (Subset Sum Problem), Dynamic Programming | Set 23 (Bellman–Ford Algorithm), Dynamic Programming | Set 16 (Floyd Warshall Algorithm), Total number of non-decreasing numbers with n digits, Vertex Cover Problem | Set 2 (Dynamic Programming Solution for Tree), Smallest power of 2 greater than or equal to n, Travelling Salesman Problem | Set 1 (Naive and Dynamic Programming), Travelling Salesman Problem | Set 2 (Approximate using MST), Find subarray with given sum | Set 2 (Handles Negative Numbers), Largest subarray with equal number of 0s and 1s, Find four elements a, b, c and d in an array such that a+b = c+d, Given an array A[] and a number x, check for pair in A[] with sum as x, Union and Intersection of two Linked Lists, Find whether an array is subset of another array | Added Method 3, Top 20 Hashing Technique based Interview Questions, Commonly Asked Algorithm Interview Questions | Set 1, Notes – Master Theorem problems and answers, Principle of programming languages | Set 1, Commonly Asked Data Structure Interview Questions | Set 1, Privileged and Non-Privileged Instructions, Process Table and Process Control Block (PCB), Difference between dispatcher and scheduler, Program for FCFS Scheduling | Set 2 (Processes with different arrival times), Program for preemptive priority CPU scheduling, Program for Round Robin scheduling | Set 1, Round Robin Scheduling with different arrival times, Program for Shortest Job First (or SJF) scheduling | Set 1 (Non- preemptive), Program for Shortest Job First (SJF) scheduling | Set 2 (Preemptive), Shortest Job First scheduling with predicted burst time, Longest Remaining Time First (LRTF) algorithm, Longest Remaining Time First (LRTF) Program, Highest Response Ratio Next (HRRN) Scheduling, Priority Scheduling with different arrival time | Set 2, Starvation and Aging in Operating Systems, Process-based and Thread-based Multitasking, Monolithic Kernel and key differences from Microkernel, Difference between multitasking, multithreading and multiprocessing, Operating System | Process Synchronization | Set 2, Peterson’s Algorithm for Mutual Exclusion | Set 1 (Basic C implementation), Peterson’s Algorithm for Mutual Exclusion | Set 2 (CPU Cycles and Memory Fence), Peterson’s Algorithm (Using processes and shared memory), Readers-Writers Problem | Set 1 (Introduction and Readers Preference Solution), Producer Consumer Problem using Semaphores | Set 1, Producer-Consumer solution using Semaphores in Java | Set 2, Dining-Philosophers Solution Using Monitors, Dining Philosopher Problem Using Semaphores.