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The matching pursuit is an example of a greedy algorithm applied on signal approximation. A greedy algorithm finds the optimal solution to Malfatti's problem of finding three disjoint circles within a given triangle that maximize the total area of the circles; it is conjectured that the same greedy algorithm is optimal for any number of circles.
The greedy randomized adaptive search procedure (also known as GRASP) is a metaheuristic algorithm commonly applied to combinatorial optimization problems. GRASP typically consists of iterations made up from successive constructions of a greedy randomized solution and subsequent iterative improvements of it through a local search . [ 1 ]
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values.
This is called the vertex search greedoid and is a kind of antimatroid. Consider a finite, directed graph D rooted at r. Let the ground set be the (directed) edges of D and the feasible sets be the edge sets of each directed subtree rooted at r with all edges pointing away from r. This is called the line search greedoid, or directed branching ...
Longest-processing-time-first (LPT) is a greedy algorithm for job scheduling.The input to the algorithm is a set of jobs, each of which has a specific processing-time.There is also a number m specifying the number of machines that can process the jobs.
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The algorithm has several stages. First, find a solution using greedy algorithm. In each iteration of the greedy algorithm the tentative solution is added the set which contains the maximum residual weight of elements divided by the residual cost of these elements along with the residual cost of the set.