Greedy Algorithm
A class of algorithmic strategies that selects the currently optimal option at each step. In multi-armed bandits it corresponds to always selecting the arm with the highest empirical mean without additional exploration, and it is a special case of the EL-F algorithm when the empirical likelihood radius is zero.
Timeline (last 90 days)
For heterogeneous asynchronous MMAB, a polynomial-time greedy algorithm can achieve a 1/e approximation ratio relative to the optimal centralized assignment.
In heterogeneous asynchronous MMAB, if players are processed in decreasing order of their activation probabilities, the approximation ratio of the greedy algorithm can be improved to 1/√e.
The greedy algorithm in this paper is a centralized offline algorithm that requires knowing all players' activation probabilities; it does not directly solve the distributed online learning problem, but rather serves as a performance benchmark (oracle baseline).
The study solves for the dominating set on the sentence hypergraph via a greedy algorithm, and the resulting dominating-set sentences constitute the final extractive summary.
A greedy approximation strategy for solving the dominating set can be completed in polynomial time, and its solution size is at most O(ln n) times the optimal solution.
In the hypergraph setting, the greedy algorithm at each step preferentially selects the sentence node participating in the greatest number of hyperedges, which is equivalent to preferentially selecting the sentence covering the most key topics.
All Facts (6)
For heterogeneous asynchronous MMAB, a polynomial-time greedy algorithm can achieve a 1/e approximation ratio relative to the optimal centralized assignment.
50%UnverifiedIn heterogeneous asynchronous MMAB, if players are processed in decreasing order of their activation probabilities, the approximation ratio of the greedy algorithm can be improved to 1/√e.
50%UnverifiedThe greedy algorithm in this paper is a centralized offline algorithm that requires knowing all players' activation probabilities; it does not directly solve the distributed online learning problem, but rather serves as a performance benchmark (oracle baseline).
50%UnverifiedThe study solves for the dominating set on the sentence hypergraph via a greedy algorithm, and the resulting dominating-set sentences constitute the final extractive summary.
50%UnverifiedA greedy approximation strategy for solving the dominating set can be completed in polynomial time, and its solution size is at most O(ln n) times the optimal solution.
50%UnverifiedIn the hypergraph setting, the greedy algorithm at each step preferentially selects the sentence node participating in the greatest number of hyperedges, which is equivalent to preferentially selecting the sentence covering the most key topics.
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