# Complete Binary Tree vs. Full Binary Tree

## What's the Difference?

A complete binary tree is a binary tree in which all levels, except possibly the last one, are completely filled, and all nodes are as far left as possible. In other words, it is a binary tree in which all nodes are filled from left to right. On the other hand, a full binary tree is a binary tree in which every node has either 0 or 2 children. This means that every level of the tree, except possibly the last one, is completely filled. In summary, while both complete and full binary trees have certain levels that are completely filled, a complete binary tree can have nodes that are not as far left as possible, whereas a full binary tree can have nodes with only 0 or 2 children.

## Comparison

Attribute | Complete Binary Tree | Full Binary Tree |
---|---|---|

Definition | A binary tree in which all levels are completely filled except possibly for the last level, which is filled from left to right. | A binary tree in which every node has either 0 or 2 children. |

Number of Nodes | Can have any number of nodes, but the last level may not be completely filled. | Can have any number of nodes, but every node has either 0 or 2 children. |

Shape | May not be perfectly balanced, as the last level may not be completely filled. | Can be perfectly balanced, as every node has either 0 or 2 children. |

Height | Height can vary depending on the number of nodes and the structure of the tree. | Height can vary depending on the number of nodes and the structure of the tree. |

Node Insertion | New nodes are inserted from left to right in the last level. | New nodes can be inserted at any available position in the tree. |

Node Deletion | Deletion can be performed from any position in the tree. | Deletion can be performed from any position in the tree. |

## Further Detail

### Introduction

Binary trees are fundamental data structures in computer science and have various applications in algorithms and data storage. Two common types of binary trees are complete binary trees and full binary trees. While they share some similarities, they also have distinct attributes that set them apart. In this article, we will explore the characteristics of complete binary trees and full binary trees, highlighting their similarities and differences.

### Complete Binary Trees

A complete binary tree is a binary tree in which all levels, except possibly the last one, are completely filled, and all nodes are as far left as possible. This means that all levels of the tree are filled from left to right, leaving no gaps. In other words, a complete binary tree is a binary tree in which all nodes are at the deepest level or the second deepest level, and all nodes at the deepest level are to the left of the nodes at the second deepest level.

One important attribute of complete binary trees is that they can be efficiently represented using an array. By assigning indices to the nodes of the tree, we can store the tree in an array such that the parent of a node at index i is located at index floor(i/2), and its left and right children are located at indices 2i and 2i+1, respectively. This property allows for efficient memory usage and easy traversal of complete binary trees.

Complete binary trees also have a predictable shape, which makes it easier to reason about their properties. The number of nodes in a complete binary tree with height h can be calculated as 2^(h+1) - 1. This property allows us to determine the height of a complete binary tree based on the number of nodes it contains, or vice versa.

Another interesting characteristic of complete binary trees is that they can be efficiently constructed from a given array of elements. This process, known as heap construction, involves building a complete binary tree by repeatedly inserting elements from the array in a specific order. Heap construction is widely used in algorithms such as heap sort and priority queues.

In summary, complete binary trees are binary trees in which all levels, except possibly the last one, are completely filled, and all nodes are as far left as possible. They can be efficiently represented using an array, have a predictable shape, and can be efficiently constructed from an array.

### Full Binary Trees

A full binary tree, also known as a proper binary tree, is a binary tree in which every node has either zero or two children. In other words, every node in a full binary tree either has no children (a leaf node) or has exactly two children. This property distinguishes full binary trees from other types of binary trees, such as complete binary trees and perfect binary trees.

One important attribute of full binary trees is that they have a unique property called the "2n - 1 rule." This rule states that the number of nodes in a full binary tree with height h is equal to 2^(h+1) - 1. This property allows us to determine the height of a full binary tree based on the number of nodes it contains, or vice versa.

Full binary trees are often used in data structures and algorithms that require balanced trees. The balanced nature of full binary trees ensures efficient search, insertion, and deletion operations. For example, binary search trees (BSTs) are commonly implemented as full binary trees to maintain their balanced structure and optimize search operations.

Another interesting characteristic of full binary trees is that they can be efficiently constructed from a given pre-order traversal and in-order traversal of the tree. This process, known as tree reconstruction, involves recursively dividing the pre-order and in-order traversals to construct the tree. This property allows for efficient construction of full binary trees from their traversals.

In summary, full binary trees are binary trees in which every node has either zero or two children. They have the "2n - 1 rule," which allows us to determine the number of nodes or the height of the tree based on either attribute. Full binary trees are commonly used in balanced data structures and can be efficiently constructed from their traversals.

### Similarities

Although complete binary trees and full binary trees have distinct attributes, they also share some similarities. Both types of trees can be represented using arrays, allowing for efficient memory usage and traversal. Additionally, both complete binary trees and full binary trees have predictable shapes, which makes it easier to reason about their properties.

Furthermore, both complete binary trees and full binary trees have a relationship between the number of nodes and the height of the tree. Complete binary trees follow the formula 2^(h+1) - 1, while full binary trees follow the formula 2^(h+1) - 1. This similarity allows us to determine the height of the tree based on the number of nodes or vice versa.

### Conclusion

Complete binary trees and full binary trees are two common types of binary trees with distinct attributes. Complete binary trees are characterized by having all levels, except possibly the last one, completely filled, and all nodes as far left as possible. They can be efficiently represented using arrays, have a predictable shape, and can be efficiently constructed from an array. On the other hand, full binary trees are characterized by every node having either zero or two children. They have the "2n - 1 rule," which allows us to determine the number of nodes or the height of the tree based on either attribute. Full binary trees are commonly used in balanced data structures and can be efficiently constructed from their traversals.

Despite their differences, complete binary trees and full binary trees also share similarities. Both types of trees can be represented using arrays, allowing for efficient memory usage and traversal. Additionally, both complete binary trees and full binary trees have predictable shapes, which makes it easier to reason about their properties. Furthermore, both complete binary trees and full binary trees have a relationship between the number of nodes and the height of the tree.

In conclusion, understanding the attributes of complete binary trees and full binary trees is essential for designing efficient algorithms and data structures. By leveraging their unique properties, developers can optimize memory usage, traversal, and construction of binary trees, leading to more efficient and scalable solutions.

Comparisons may contain inaccurate information about people, places, or facts. Please report any issues.