Greedy Algorithms in Computer Science

Algorithms

Greedy Algorithms

Definition: A greedy algorithm is a simple, intuitive algorithm that solves a problem by making the locally optimal choice at each step, hoping that these local choices will lead to a global optimum solution.

Key Characteristics:

• Makes the locally optimal choice at each step
• Does not consider the long-term consequences of its choices
• Does not backtrack or reconsider previous choices

Examples:

• Coin changing problem: making change for a given amount using the fewest coins possible
• Activity selection problem: selecting the optimal set of activities to perform given a set of constraints
• Huffman coding: a variable-length prefix code that assigns shorter codes to more frequent symbols

Advantages:

• Fast and efficient
• Easy to implement

Disadvantages:

• May not always find the global optimum solution
• Can get stuck in a local optimum

Backtracking

Definition: Backtracking is an algorithmic-technique for solving problems recursively by trying to build a solution incrementally, removing solutions that fail to satisfy the constraints of the problem at any point of time (by time, here, is referred to the time elapsed till reaching any level of the search tree).

Key Characteristics:

• Recursively explores the solution space
• Backtracks when a dead end is reached
• Uses a depth-first search strategy

Examples:

• N-Queens problem: placing N queens on an NxN chessboard such that no queen attacks another
• Sudoku: filling a 9x9 grid with numbers from 1 to 9 such that each row, column, and 3x3 sub-grid contains each number exactly once
• Cryptarithmetic puzzles: solving mathematical puzzles that involve words and numbers

Advantages:

• Can be used to solve complex problems
• Can be used to find all possible solutions

Disadvantages:

• Can be computationally expensive
• May take a long time to find a solution

Algorithms

Greedy Algorithms

• A greedy algorithm makes the locally optimal choice at each step, hoping to find a global optimum solution.
• It does not consider the long-term consequences of its choices and does not backtrack or reconsider previous choices.
• Examples of greedy algorithms include:
  • Coin changing problem: making change for a given amount using the fewest coins possible.
  • Activity selection problem: selecting the optimal set of activities to perform given a set of constraints.
  • Huffman coding: a variable-length prefix code that assigns shorter codes to more frequent symbols.
• Greedy algorithms are fast and efficient, but may not always find the global optimum solution and can get stuck in a local optimum.

Backtracking

• Backtracking is an algorithmic-technique that recursively explores the solution space, removing solutions that fail to satisfy the constraints of the problem.
• It uses a depth-first search strategy and backtracks when a dead end is reached.
• Examples of backtracking algorithms include:
  • N-Queens problem: placing N queens on an NxN chessboard such that no queen attacks another.
  • Sudoku: filling a 9x9 grid with numbers from 1 to 9 such that each row, column, and 3x3 sub-grid contains each number exactly once.
  • Cryptarithmetic puzzles: solving mathematical puzzles that involve words and numbers.
• Backtracking algorithms can be used to solve complex problems and find all possible solutions, but can be computationally expensive and may take a long time to find a solution.

Learn about greedy algorithms, their characteristics, and examples. Understand how they solve problems by making locally optimal choices at each step.