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  2. Greedoid - Wikipedia

    en.wikipedia.org/wiki/Greedoid

    A greedy algorithm is optimal for every R-compatible linear objective function over a greedoid. The intuition behind this proposition is that, during the iterative process, each optimal exchange of minimum weight is made possible by the exchange property, and optimal results are obtainable from the feasible sets in the underlying greedoid.

  3. Farthest-first traversal - Wikipedia

    en.wikipedia.org/wiki/Farthest-first_traversal

    The farthest-first traversal of a finite point set may be computed by a greedy algorithm that maintains the distance of each point from the previously selected points, performing the following steps: [3] Initialize the sequence of selected points to the empty sequence, and the distances of each point to the selected points to infinity.

  4. Set cover problem - Wikipedia

    en.wikipedia.org/wiki/Set_cover_problem

    This greedy algorithm actually achieves an approximation ratio of (′) where ′ is the maximum cardinality set of . For δ − {\displaystyle \delta -} dense instances, however, there exists a c ln ⁡ m {\displaystyle c\ln {m}} -approximation algorithm for every c > 0 {\displaystyle c>0} .

  5. Optimal substructure - Wikipedia

    en.wikipedia.org/wiki/Optimal_substructure

    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.

  6. Category:Greedy algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Greedy_algorithms

    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.

  7. Weighted matroid - Wikipedia

    en.wikipedia.org/wiki/Weighted_matroid

    The notion of matroid has been generalized to allow for other types of sets on which a greedy algorithm gives optimal solutions; see greedoid and matroid embedding for more information. Korte and Lovász would generalize these ideas to objects called greedoids , which allow even larger classes of problems to be solved by greedy algorithms.

  8. Assignment problem - Wikipedia

    en.wikipedia.org/wiki/Assignment_problem

    This algorithm may yield a non-optimal solution. For example, suppose there are two tasks and two agents with costs as follows: Alice: Task 1 = 1, Task 2 = 2. George: Task 1 = 5, Task 2 = 8. The greedy algorithm would assign Task 1 to Alice and Task 2 to George, for a total cost of 9; but the reverse assignment has a total cost of 7.

  9. Acute and obtuse triangles - Wikipedia

    en.wikipedia.org/wiki/Acute_and_obtuse_triangles

    An acute triangle (or acute-angled triangle) is a triangle with three acute angles (less than 90°). An obtuse triangle (or obtuse-angled triangle) is a triangle with one obtuse angle (greater than 90°) and two acute angles. Since a triangle's angles must sum to 180° in Euclidean geometry, no Euclidean triangle can have more than one obtuse ...