**1. Basic Greedy Problems**

· Fractional Knapsack Problem: Maximize the value in a knapsack with fractional items.

· Activity Selection Problem: Select the maximum number of activities that don’t overlap.

· Job Sequencing Problem: Schedule jobs to maximize profit with deadlines.

· Huffman Coding: Construct an optimal prefix code for a set of characters.

· Minimum Number of Coins: Find the minimum number of coins for a given amount.

· Maximum Subarray Sum (Kadane's Algorithm): Find the maximum sum of a contiguous subarray.

· Change-Making Problem: Compute the minimum number of coins for change.

· Greedy Coloring of Graphs: Color a graph using the minimum number of colours.

· Minimum Spanning Tree (Kruskal’s Algorithm): Find the minimum spanning tree of a graph.

· Minimum Spanning Tree (Prim’s Algorithm): Another method to find the minimum spanning tree.

** 2. Interval and Scheduling Problems**

· Interval Scheduling Maximization: Find the maximum number of non-overlapping intervals.

· Job Scheduling with Deadlines: Schedule jobs to maximize profit with deadlines.

· Weighted Interval Scheduling: Maximize profit with intervals having weights and deadlines.

· Minimum Number of Platforms: Find the minimum number of platforms required for a train schedule.

· Event Scheduling: Schedule events to maximize the number of non-overlapping events.

· Conference Room Scheduling: Find the minimum number of rooms required for a conference.

** 3. Graph Problems**

· Dijkstra’s Shortest Path Algorithm: Find the shortest path from a source to all nodes.

· Prim’s Algorithm for MST: Find the minimum spanning tree using a priority queue.

· Kruskal’s Algorithm for MST: Find the minimum spanning tree using edge sorting and union-find.

· Shortest Path with a Fixed Number of Edges: Find the shortest path with exactly k edges.

· Traveling Salesman Problem (TSP) Approximation: Find a near-optimal solution for TSP.

· Maximum Flow in a Network: Find the maximum flow using algorithms like Ford-Fulkerson.

· Minimum Cut in a Network: Find the minimum cut in a flow network.

· Shortest Path in Weighted Graphs (Greedy Approach): Find shortest paths with a modified greedy approach.

** 4. Partitioning and Selection Problems**

· Greedy Set Cover: Find a minimum number of sets to cover a universe of elements.

· Kth Largest Element: Find the Kth largest element in an unsorted array.

· Partition Problem: Determine if a set can be partitioned into subsets with equal sum.

· Maximum Sum of Non-Adjacent Elements: Find the maximum sum of non-adjacent elements in an array.

· Weighted Job Scheduling: Schedule jobs with weights to maximize total profit.

· Greedy Knapsack Problem: Solve the knapsack problem with fractional values.

** 5. String and Text Processing**

· Greedy String Matching: Find patterns in strings using a greedy approach.

· Minimum Window Substring: Find the smallest substring containing all characters of another string.

· Optimal Merge Pattern: Minimize the cost of merging multiple files.

· Longest Common Subsequence (Greedy Approximation): Find a long common subsequence with greedy methods.

· Network and Flow Problems

· Maximum Bipartite Matching: Find the maximum matching in a bipartite graph.

· Minimum Vertex Cover: Find the minimum vertex cover in a bipartite graph.

· Maximum Independent Set: Find a maximum independent set in a graph.

· Network Flow (Edmonds-Karp Algorithm): Find maximum flow using a BFS-based approach.

** 7. Geometry and Spatial Problems**

· Convex Hull: Find the convex hull of a set of points.

· Closest Pair of Points: Find the closest pair of points using a greedy approach.

· Line Intersection: Find intersections of lines using greedy algorithms.

** 8. Miscellaneous Greedy Problems**

· Minimum Cost Path with Obstacles: Find the minimum cost path in a grid with obstacles.

· Minimum Cost to Merge Stones: Merge stones with minimum cost to form one pile.

· Water Trapping: Calculate the amount of water trapped between elevations.

· Greedy Box Packing: Pack boxes in a container with minimum wasted space.

· Greedy Travel Path: Find an efficient travel path using greedy heuristics.

· Optimal Resource Allocation: Allocate resources optimally with greedy methods.

· Greedy Scheduling: Schedule tasks to minimize overall completion time.

· Greedy Stock Trading: Maximize profit from stock trading with limited transactions.

· Greedy Tournament Scheduling: Schedule tournaments to maximize efficiency.

** 9. Approximation Algorithms**

· Greedy TSP Approximation: Approximate solution for the Traveling Salesman Problem.

· Greedy Vertex Cover: Approximate the minimum vertex cover in a graph.

· Greedy Knapsack Problem: Approximate solution for the 0/1 knapsack problem.

· Greedy Graph Coloring: Approximate colouring of graphs with minimum colours.

· Greedy Set Packing: Approximate solution for the set packing problem.

** 10. Scheduling and Allocation**

· Job Shop Scheduling: Allocate jobs to machines with minimum makespan.

· Resource Allocation: Allocate resources to maximize utilization.

· Interval Partitioning: Partition intervals into a minimum number of resources.

· Load Balancing: Distribute tasks to minimize the maximum load.

** 11. Array and Sequence Optimization**

· Minimum Difference Partition: Partition an array to minimize the difference between sums.

· Largest Sum of K Contiguous Elements: Find the largest sum of k contiguous elements.

· Minimum Cost to Cut a Rod: Find the minimum cost of cutting a rod into pieces.

· Greedy Array Rearrangement: Rearrange an array to maximize a certain metric.

** 12. Resource Management**

· Greedy Load Balancing: Distribute resources to minimize the maximum load.

· Greedy Job Scheduling with Deadlines: Schedule jobs to maximize profit by deadlines.

· Optimal Resource Allocation for Tasks: Allocate resources to tasks optimally.

** 13. Graph Approximation**

· Greedy Approximation for Steiner Tree: Approximate solution for the Steiner tree problem.

· Greedy Solution for the Set Cover Problem: Approximate set cover with greedy methods.

· Greedy Approach for Facility Location: Approximate facility location problems.

** 14. Financial and Operational Optimization**

· Greedy Investment Strategy: Maximize returns from investments using greedy methods.

· Greedy Pricing Strategy: Set prices to maximize revenue.

· Greedy Supply Chain Management: Optimize supply chain operations with greedy algorithms.

** 15. Network Design**

· Greedy Network Design: Design efficient networks with minimum cost.

· Greedy Communication Scheduling: Schedule communication to maximize efficiency.

16. Computational Geometry

· Greedy Point Selection: Select points to cover a region with minimal number of points.

· Greedy Polygon Triangulation: Triangulate a polygon using greedy methods.

**
17. Computational Scheduling**

· Greedy Task Scheduling: Schedule tasks to minimize completion time.

· Greedy Job Allocation: Allocate jobs to minimize total processing time.

**18. Multi-Objective Optimization**

· Greedy Multi-Objective Scheduling: Schedule tasks to optimize multiple objectives.

· Greedy Multi-Objective Resource Allocation: Allocate resources to optimize multiple metrics.

19. Game Theory and Strategic Planning

· Greedy Game Strategy: Develop strategies for games using greedy algorithms.

· Greedy Resource Allocation in Games: Allocate resources optimally in-game scenarios.

20. Advanced Greedy Algorithms

20. Advanced Greedy Algorithms

· Greedy Approach for Maximum Independent Set: Approximate solution for the maximum independent set.

· Greedy Algorithm for Vertex Cover in Non-Bipartite Graphs: Approximate vertex cover in general graphs.

· Greedy Algorithm for Shortest Path with Constraints: Find the shortest path under constraints.

· Greedy Algorithm for Network Flow with Multiple Sources: Solve network flow problems with multiple sources.

· Greedy Algorithm for Weighted Graph Coloring: Approximate graph coloring with weights.

· Greedy Algorithm for Cutting Stock Problem: Solve cutting stock problems with greedy methods.

· Greedy Approach for Vehicle Routing Problem: Approximate solution for vehicle routing.

· Greedy Algorithm for Multi-Dimensional Knapsack Problem: Approximate multi-dimensional knapsack solutions.

· Greedy Algorithm for Optimal Path Planning: Find optimal path planning using greedy heuristics.

· Greedy Algorithm for Multiple Knapsack Problem: Solve multiple knapsack problems with greedy methods.

· Greedy Algorithm for Bandwidth Allocation: Optimize bandwidth allocation with greedy approaches.

· Greedy Approach for Scheduling with Resource Constraints: Schedule tasks considering resource constraints.

· Greedy Algorithm for Weighted Job Scheduling: Maximize profit with weighted job scheduling.

· Greedy Algorithm for Large-Scale Network Optimization: Optimize large-scale networks with greedy methods.

· Greedy Algorithm for Task Assignment in Parallel Systems: Assign tasks in parallel systems optimally.

· Greedy Algorithm for Optimal Sequencing: Sequence tasks or events optimally.

· Greedy Approach for Inventory Management: Optimize inventory levels using greedy algorithms.

· Greedy Algorithm for Optimal Time Management: Manage time effectively with greedy approaches.

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