This method of storage is often used for binary heaps.

We should push to the Binary Heap associated to node.

A B-heap is a binary heap implemented to keep subtrees in a single page.

In contrast to a binary heap, a leftist tree attempts to be very unbalanced.

Full and almost full binary heaps may be represented in a very space-efficient way using an array alone.

A binary heap is a heap data structure created using a binary tree.

Skew heaps are advantageous because of their ability to merge more quickly than binary heaps.

Like a binary heap, a min-max heap is represented as a complete binary tree.

They concluded that the pairing heap is as fast as, and often faster than, other efficient data structures like the binary heaps.

Python has a [2] module that implements a priority queue using a binary heap.

B-heaps are binary heaps that keep subtrees in a single page, reducing the number of pages accessed by up to a factor of ten.

The operation of merging two binary heaps takes Θ(n) for equal-sized heaps.

The data structure resulting from this random choice is called a treap, due to its combination of binary search tree and binary heap features.

In computer science, a min-max heap is a double-ended priority queue implemented as a modified version of a binary heap.

This data structure allows decrease priority operations to be performed more quickly than binary heaps, at the expense of slower delete minimum operations.

In computer science, a binomial heap is a heap similar to a binary heap but also supports quick merging of two heaps.

In contrast with binary heaps, there are no structural constraints, so there is no guarantee that the height of the tree is logarithmic.

Although Fenwick trees are trees in concept, in practice they are implemented using a flat array analogous to implementations of a binary heap.

Unlike heapsort, smoothsort does not use a binary heap, but rather a custom heap based on the Leonardo numbers L(n).

With a self-balancing binary search tree or binary heap, the algorithm requires time (which is dominated by , assuming the graph is connected).

The binary heap uses O(log n) time for both operations, but also allow queries of the element of highest priority without removing it in constant time.

Ternary heapsort uses a ternary heap instead of a binary heap; that is, each element in the heap has three children.

Unlike a binary heap, though, the nodes in this tree do not obey the min-heap property; rather they obey the min-max heap property.

Binary trees are used to implement binary search trees and binary heaps, finding applications in efficient searching and sorting algorithms.

A binomial heap is implemented as a collection of binomial trees (compare with a binary heap, which has a shape of a single binary tree).