quick sort am i correct?
yes pagal
This is really dependent how your queue is implemented. Unfortunately, working from the outside of a queue, this is usually impossible without removing all elements from the queue. while !queue.isEmpty() print queue.dequeue()
In linked queue we're dynamically allocating the memory and there's no fixed memory limit in Linked Queue. That's why there's no operation for overflow. I guess It's the correct reason
Queue is difined as a special type of data structure .where elements are inserted one end &elements are deleted same end .The end from where they elements are inserted is called as "Rear end".The end from where elements are deleted is called "Front end". A linear queue is akin to the queue at the post office: it can be envisioned to be linear in space, and limited in space. When a linear queue of finite capacity is full, new arrivals are turned away (elements cannot be added, post office customers walk away). A circular queue behaves just like a normal queue, but is typically implemented in a a structure akin to a circle. The typical behavior is that the circular queue, when full, does not turn new entries away, but makes space by removing the oldest element in the queue. Of course, a queue might also be implemented to be of infinite, or virtually infinite, capacity.
There are 2 conditions for queue full if queue is implemented using arrays. First condition is Front = 1 and Rear = N Second condition is Front = Rear + 1
yes pagal
bring the police they will be in que by themselves
This is really dependent how your queue is implemented. Unfortunately, working from the outside of a queue, this is usually impossible without removing all elements from the queue. while !queue.isEmpty() print queue.dequeue()
In linked queue we're dynamically allocating the memory and there's no fixed memory limit in Linked Queue. That's why there's no operation for overflow. I guess It's the correct reason
The priority queue decrease key operation can be efficiently implemented by using a data structure like a binary heap or a Fibonacci heap. These data structures allow for the key of a specific element in the priority queue to be decreased in logarithmic time complexity, making the operation efficient.
A circular queue is similar to the normal queue with the difference that queue is circular queue ; that is pointer rear can point to beginning of the queue when it reaches at the end of the queue. Advantage of this type of queue is that empty location let due to deletion of elements using front pointer can again be filled using rear pointer. There are 2 conditions for queue full if queue is implemented using arrays. First condition is Front = 1 and Rear = N Second condition is Front = Rear + 1
Queue is difined as a special type of data structure .where elements are inserted one end &elements are deleted same end .The end from where they elements are inserted is called as "Rear end".The end from where elements are deleted is called "Front end". A linear queue is akin to the queue at the post office: it can be envisioned to be linear in space, and limited in space. When a linear queue of finite capacity is full, new arrivals are turned away (elements cannot be added, post office customers walk away). A circular queue behaves just like a normal queue, but is typically implemented in a a structure akin to a circle. The typical behavior is that the circular queue, when full, does not turn new entries away, but makes space by removing the oldest element in the queue. Of course, a queue might also be implemented to be of infinite, or virtually infinite, capacity.
The Breadth-First Search (BFS) algorithm can be implemented using recursion by using a queue data structure to keep track of the nodes to visit. The algorithm starts by adding the initial node to the queue and then recursively visits each neighbor of the current node, adding them to the queue. This process continues until all nodes have been visited.
There are 2 conditions for queue full if queue is implemented using arrays. First condition is Front = 1 and Rear = N Second condition is Front = Rear + 1
Yes, Breadth-First Search (BFS) can be implemented recursively, but it is not the most efficient method compared to using a queue-based iterative approach.
No, Breadth-First Search (BFS) is not inherently recursive. It is typically implemented using a queue data structure rather than recursion.
To efficiently implement the decrease-key operation in a priority queue, you can use a data structure like a binary heap or Fibonacci heap. These data structures allow for efficient updates to the priority queue while maintaining the heap property, which helps optimize performance.