Greedy algorithm

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Greedy algorithm

A greedy algorithm is a algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most immediate benefit. Greedy algorithms are used for solving optimization problems and are known for their simplicity and efficiency. However, they do not always produce the optimal solution for all problems.

Characteristics[edit | edit source]

Greedy algorithms follow a problem-solving heuristic of making the locally optimal choice at each stage with the hope of finding a global optimum. The main characteristics of greedy algorithms include:

  • **Greedy choice property**: A global optimum can be arrived at by selecting a local optimum.
  • **Optimal substructure**: An optimal solution to the problem contains an optimal solution to subproblems.

Applications[edit | edit source]

Greedy algorithms are widely used in various fields, including:

Examples[edit | edit source]

Some well-known examples of greedy algorithms include:

  • **Prim's algorithm**: Used to find the minimum spanning tree of a graph.
  • **Kruskal's algorithm**: Another algorithm for finding the minimum spanning tree.
  • **Dijkstra's algorithm**: Used for finding the shortest path between nodes in a graph.
  • **Huffman coding**: Used for lossless data compression.

Advantages and Disadvantages[edit | edit source]

Advantages[edit | edit source]

  • **Simplicity**: Greedy algorithms are often easier to understand and implement.
  • **Efficiency**: They can be more efficient in terms of time and space complexity for certain problems.

Disadvantages[edit | edit source]

  • **Non-optimal solutions**: Greedy algorithms do not always produce the optimal solution.
  • **Problem-specific**: They are not universally applicable and work only for problems with the greedy choice property and optimal substructure.

Related Pages[edit | edit source]

See Also[edit | edit source]

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Contributors: Prab R. Tumpati, MD