Greedy Search Algorithm
A Greedy Search Algorithm is a heuristic iterative search algorithm that makes the locally optimal choices at each iteration.
- Example(s):
- Counter-Example(s):
- See: Learning as Search, Rule Learning, Optimization Algorithm, Gradient Descent Algorithm, Traveling Salesman Problem.
References
2022a
- (Gees4Geeks, 2022) ⇒ https://www.geeksforgeeks.org/greedy-algorithms/ Retrieved:2022-4-2.
- QUOTE: Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. So the problems where choosing locally optimal also leads to global solution are best fit for Greedy.
For example consider the Fractional Knapsack Problem. The local optimal strategy is to choose the item that has maximum value vs weight ratio. This strategy also leads to global optimal solution because we allowed to take fractions of an item.
- QUOTE: Greedy is an algorithmic paradigm that builds up a solution piece by piece, always choosing the next piece that offers the most obvious and immediate benefit. So the problems where choosing locally optimal also leads to global solution are best fit for Greedy.
2022b
- (Wikipedia, 2022) ⇒ https://en.wikipedia.org/wiki/Greedy_algorithm Retrieved:2022-4-2.
- A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage.[1] In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time.
For example, a greedy strategy for the travelling salesman problem (which is of high computational complexity) is the following heuristic: "At each step of the journey, visit the nearest unvisited city." This heuristic does not intend to find the best solution, but it terminates in a reasonable number of steps; finding an optimal solution to such a complex problem typically requires unreasonably many steps. In mathematical optimization, greedy algorithms optimally solve combinatorial problems having the properties of matroids and give constant-factor approximations to optimization problems with the submodular structure.
- A greedy algorithm is any algorithm that follows the problem-solving heuristic of making the locally optimal choice at each stage.[1] In many problems, a greedy strategy does not produce an optimal solution, but a greedy heuristic can yield locally optimal solutions that approximate a globally optimal solution in a reasonable amount of time.
- ↑ Black, Paul E. (2 February 2005). "greedy algorithm". Dictionary of Algorithms and Data Structures. U.S. National Institute of Standards and Technology (NIST). Retrieved 17 August 2012
2017
- (Sammut, 2017) ⇒ Sammut C. (2017) "Greedy Search". In: Sammut C., Webb G.I. (eds) "Encyclopedia of Machine Learning and Data Mining." Springer, Boston, MA
- QUOTE: At each step in its search, a greedy algorithm makes the best decision it can at the time and continues without backtracking. For example, an algorithm may perform a general-to-specific search and at each step, commits itself to the specialization that best fits that training data, so far. It continues without backtracking to change any of its decisions. Greedy algorithms are used in many machine-learning algorithms, including decision tree learning (Breiman et al. 1984; Quinlan 1993) and rule learning algorithms, such as sequential covering.