Prerequsites : Reorganize String
In the previous two questions Reorganize String and Ugly number. We determined that a problem structure demanded processing of the minimum or maximum to build our solution—values with the higest prioirty. We greedily chose that value and, if it needed to be processed again, added it back to the heap. The heap then handled the dynamic changing of priority of the elements added back and the elements available. (which characters have should be added to the string next for Reorganize String and what are the next numbers needed to be multiplied with prime factors in Ugly number ).This is more efficient than sorting the values every time a value is processed and adding back to the set of choices. Using a brute force approach, we would need to resort the whole array to keep up with the ever-changing priority, which takes O(nlogn) for each iteration instead of O(logn), thus saving time.
Similar questions
These questions can be difficult to identify from the description. A bit of reframing the question can help us determine if there is a priority among the possible arrangements. We will consider selecting the highest priority value greedily and supporting our venture with a heap. Here are some questions to build your intuition on concepts like this.
- Task Scheduler —The algorithm uses a priority queue(heap) to greedily select the most repeated task available, and a cool down queue to ensure only tasks available(not in the cool down period) for scheduling are in the priority queue. Simillar to Reorganize String
- Rearrange String k Distance Apart— a greedy algorithm to rearrange a string by considering the character with the highest frequency first, using a max heap sorted based on the frequency of the characters in the string. It also uses a FIFO queue to store characters that have been added but are frozen due to the distance limitation. The time complexity is O(nlogn) and the space complexity is O(1).
- Meeting Rooms II — Using a Greedy approach, we can allocate rooms for meetings efficiently. We store the end times of meetings in various rooms in a min-heap and check the topmost element to determine the room that will become available earliest. When allocating a room for a new meeting, we prioritize the room that needs to be freed earliest. If it is not yet available, we must allocate a different room. The size of the heap indicates the maximum number of rooms required.
- Greedy Graph Problems(Graph knowldge recomended) — Djikstara, Prim
- They involve making a sequence of decisions, each of which has an associated cost. The goal is to find the sequence of decisions that minimizes the total cost. Usually achived in conjunction with a heap.
- If you're looking for more questions like this, I recommend going to the LeetCode section with tags and selecting both greedy and priority queue.
Greedy algorithms build up a solution step-by-step, choosing the piece that offers the most benefit. They are used to solve optimization problems, where the best choice at each step leads to an optimal solution. If the best value is continuiously being, being replaced by the ones smaller than it. A heap might be used to handle the partial sortedness of the underlying data pipeline
Extra:
Solution : Task Scheduler