Greedy algorithms make locally optimal choices for a global solution from "summary" of Data Structures and Algorithms in Python by Michael T. Goodrich,Roberto Tamassia,Michael H. Goldwasser
Greedy algorithms are a class of algorithms for solving optimization problems that make a sequence of choices, each of which is the best choice locally at the time it is made. In other words, a greedy algorithm makes a series of choices that are each the most immediately beneficial at each decision point, with the hope that this will lead to a globally optimal solution. The key characteristic of a greedy algorithm is that once a decision is made, it is never reconsidered. The algorithm proceeds by making a series of choices, each based on the current state of the problem, without regard to how those choices will affect future decisions. At each step, a greedy algorithm selects the best available option without considering the consequences of that choice in the long term. This means that the algorithm may not always find the best possible solution to a problem, but it will find a solution that is locally optimal at each step. This can be an advantage in situations where finding the absolute best solution is impractical or computationally infeasible. Greedy algorithms are often used in situations where finding the best possible solution would require examining all possible choices, which may be prohibitively time-consuming or resource-intensive. One important feature of greedy algorithms is that they are usually relatively simple and easy to implement. Because they make decisions based solely on the current state of the problem, without considering future consequences, greedy algorithms are often more straightforward to design and analyze than more complex optimization techniques. However, this simplicity comes at a cost, as greedy algorithms may not always find the best possible solution and can sometimes produce suboptimal results. Despite their limitations, greedy algorithms are widely used in a variety of applications, including scheduling, routing, and optimization problems in computer science and beyond. In many cases, a greedy algorithm can provide a good solution quickly and efficiently, even if it is not the absolute best solution possible. By making locally optimal choices at each step, a greedy algorithm can often find a solution that is satisfactory for practical purposes, even if it is not the absolute best solution that could be found.
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