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A separate deque with threads to be executed is maintained for each processor. To execute the next thread, the processor gets the first element from the deque (using the "remove first element" deque operation). If the current thread forks, it is put back to the front of the deque ("insert element at front") and a new thread is executed.
The list holds the remaining elements (a.k.a., the rear of the queue) in reverse order. It is easy to insert into the front of the queue by adding a node at the head of f {\displaystyle f} . And, if r {\displaystyle r} is not empty, it is easy to remove from the end of the queue by removing the node at the head of r {\displaystyle r} .
For the stack, priority queue, deque, and DEPQ types, peek can be implemented in terms of pop and push (if done at same end). For stacks and deques this is generally efficient, as these operations are O (1) in most implementations, and do not require memory allocation (as they decrease the size of the data) – the two ends of a deque each ...
In computer science, a double-ended priority queue (DEPQ) [1] or double-ended heap [2] is a data structure similar to a priority queue or heap, but allows for efficient removal of both the maximum and minimum, according to some ordering on the keys (items) stored in the structure.
Here, the list [0..] represents , x^2>3 represents the predicate, and 2*x represents the output expression.. List comprehensions give results in a defined order (unlike the members of sets); and list comprehensions may generate the members of a list in order, rather than produce the entirety of the list thus allowing, for example, the previous Haskell definition of the members of an infinite list.
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A similar function can be defined for popping and then inserting, which in Python is called "heapreplace": // Extract the root of the heap, and push a new item // heap : an array representing the heap, indexed at 1 // item : an element to insert // Returns the current root of heap Replace ( heap : List<T>, item : T) -> T: swap heap [1] and item ...
Python's heapq module implements a binary min-heap on top of a list. Java 's library contains a PriorityQueue class, which implements a min-priority-queue as a binary heap. .NET 's library contains a PriorityQueue class, which implements an array-backed, quaternary min-heap.