Greedy

It's a problem-solving strategy that involves making the locally optimal choice at each step, hoping that this will lead to the overall optimal solution.

Imagine it like this: You're navigating a maze, and at each junction, you choose the path that seems most promising right now, without considering the entire maze layout.

Key characteristics:

• Local optimization: Focuses on making the best decision at each individual step, rather than considering the entire problem space at once.

• Iterative approach: Builds a solution incrementally, one step at a time.

• Not always optimal: While sometimes leading to the best solution, it doesn't guarantee it in every case.

Common examples:

• Activity selection problem: Choosing the maximum number of non-overlapping activities that can be completed within a given time frame.

• Dijkstra's shortest path algorithm: Finding the shortest path between nodes in a graph.

• Kruskal's minimum spanning tree algorithm: Constructing a minimum spanning tree for a weighted graph.

• Huffman coding: Efficient compression of data using variable-length codes.

When to consider greedy approach:

• Problems where optimal substructure property holds (optimal solution can be constructed from optimal solutions to subproblems).

• Problems where a greedy choice can be made at each step without affecting future choices.

• Problems where finding a global optimal solution is too complex or time-consuming.