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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 right example generalises to 2-colorable graphs with n vertices, where the greedy algorithm expends n/2 colors. In the study of graph coloring problems in mathematics and computer science , a greedy coloring or sequential coloring [ 1 ] is a coloring of the vertices of a graph formed by a greedy algorithm that considers the vertices of the ...
Pages in category "Greedy algorithms" The following 9 pages are in this category, out of 9 total. This list may not reflect recent changes. A. A* search algorithm; B.
In mathematics, the greedy algorithm for Egyptian fractions is a greedy algorithm, first described by Fibonacci, for transforming rational numbers into Egyptian fractions. An Egyptian fraction is a representation of an irreducible fraction as a sum of distinct unit fractions , such as 5 / 6 = 1 / 2 + 1 / 3 .
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. The LPT algorithm works as follows:
Typically, a greedy algorithm is used to solve a problem with optimal substructure if it can be proven by induction that this is optimal at each step. [1] Otherwise, provided the problem exhibits overlapping subproblems as well, divide-and-conquer methods or dynamic programming may be used.
Such algorithms cannot guarantee to return the globally optimal decision tree. To reduce the greedy effect of local optimality, some methods such as the dual information distance (DID) tree were proposed. [36] Decision-tree learners can create over-complex trees that do not generalize well from the training data. (This is known as overfitting ...
This result guarantees the optimality of many well-known algorithms. For example, a minimum spanning tree of a weighted graph may be obtained using Kruskal's algorithm, which is a greedy algorithm for the cycle matroid. Prim's algorithm can be explained by taking the line search greedoid instead.