A greedy algorithm will return as many results as possible. It depends on the algorithm what that means.
An example would be in regular expressions. The regexp "/(a.+b)/" searches for a string that starts with "a" and ends with "b". So in the string "There's a bunny in the basket" a greedy algorithm would find "a bunny in the b", while a non-greedy search would find "a b".
Yes, Dijkstra's algorithm is a greedy algorithm because it makes decisions based on the current best option without considering future consequences.
A greedy algorithm is similar to a dynamic programming algorithm, but the difference is that solutions to the subproblems do not have to be known at each stage; instead a "greedy" choice can be made of what looks best for the moment.
Greedy algorithms are proven to be optimal through various techniques, such as the exchange argument and the matroid intersection theorem. One example is the proof of the greedy algorithm for the minimum spanning tree problem, where it is shown that the algorithm always produces a tree with the minimum weight. Another example is the proof of the greedy algorithm for the activity selection problem, which demonstrates that the algorithm always selects the maximum number of compatible activities. These proofs typically involve showing that the greedy choice at each step leads to an optimal solution overall.
The time complexity of a greedy algorithm is typically O(n log n) or O(n), where n is the number of elements in the input data.
the basic difference between them is that in greedy algorithm only one decision sequence is ever generated. where as in dynamic programming many decision sequences are generated.
There is not "a" greedy algorithm; "greedy algorithm" is a term to describe several algorithms that have some things in common. The general idea is that at each step, you look for what seems to be, "locally", the best solution. For example, in a shortest-distance problem, look for a step that takes you closer to the destination. This may, or may not, lead to the best solution overall.
Both algorithms have the same efficiency and both are based on the same greedy approach. But Kruskal's algorithm is much easier to implement.
The time complexity of the knapsack greedy algorithm for solving a problem with a large number of items is O(n log n), where n is the number of items.
The greedy algorithm for the knapsack problem involves selecting items based on their value-to-weight ratio, prioritizing items with the highest ratio first. This approach aims to maximize the value of items placed in the knapsack while staying within its weight capacity. By iteratively selecting the most valuable item that fits, the greedy algorithm can provide a near-optimal solution for the knapsack problem.
Dijkstra's algorithm is used by the OSPF and the IS-IS routing protocols. The last three letters in OSPF (SPF) mean "shortest path first", which is an alternative name for Dijkstra's algorithm.
yes
That's not our job.Unless your programs are trivial, the algorithm is likely to be too complicated to explain here, and if they are trivial, then... well, then they're trivial, and you should be able to figure them out yourself.