# Binary tree

Last updated A labeled binary tree of size 9 and height 3, with a root node whose value is 2. The above tree is unbalanced and not sorted.

In computer science, a binary tree is a tree data structure in which each node has at most two children, which are referred to as the left child and the right child. A recursive definition using just set theory notions is that a (non-empty) binary tree is a tuple (L, S, R), where L and R are binary trees or the empty set and S is a singleton set containing the root.  Some authors allow the binary tree to be the empty set as well. 

## Contents

From a graph theory perspective, binary (and K-ary) trees as defined here are arborescences.  A binary tree may thus be also called a bifurcating arborescence  —a term which appears in some very old programming books,  before the modern computer science terminology prevailed. It is also possible to interpret a binary tree as an undirected, rather than a directed graph, in which case a binary tree is an ordered, rooted tree.  Some authors use rooted binary tree instead of binary tree to emphasize the fact that the tree is rooted, but as defined above, a binary tree is always rooted.  A binary tree is a special case of an ordered K-ary tree, where k is 2.

In mathematics, what is termed binary tree can vary significantly from author to author. Some use the definition commonly used in computer science,  but others define it as every non-leaf having exactly two children and don't necessarily order (as left/right) the children either. 

In computing, binary trees are used in two very different ways:

• First, as a means of accessing nodes based on some value or label associated with each node.  Binary trees labelled this way are used to implement binary search trees and binary heaps, and are used for efficient searching and sorting. The designation of non-root nodes as left or right child even when there is only one child present matters in some of these applications, in particular, it is significant in binary search trees.  However, the arrangement of particular nodes into the tree is not part of the conceptual information. For example, in a normal binary search tree the placement of nodes depends almost entirely on the order in which they were added, and can be re-arranged (for example by balancing) without changing the meaning.
• Second, as a representation of data with a relevant bifurcating structure. In such cases, the particular arrangement of nodes under and/or to the left or right of other nodes is part of the information (that is, changing it would change the meaning). Common examples occur with Huffman coding and cladograms. The everyday division of documents into chapters, sections, paragraphs, and so on is an analogous example with n-ary rather than binary trees.

## Definitions

### Recursive definition

To actually define a binary tree in general, we must allow for the possibility that only one of the children may be empty. An artifact, which in some textbooks is called an extended binary tree is needed for that purpose. An extended binary tree is thus recursively defined as: 

• the empty set is an extended binary tree
• if T1 and T2 are extended binary trees, then denote by T1 • T2 the extended binary tree obtained by adding a root r connected to the left to T1 and to the right to T2[ clarification needed where did the 'r' go in the 'T1 • T2' symbol] by adding edges when these sub-trees are non-empty.

Another way of imagining this construction (and understanding the terminology) is to consider instead of the empty set a different type of node—for instance square nodes if the regular ones are circles. 

### Using graph theory concepts

A binary tree is a rooted tree that is also an ordered tree (a.k.a. plane tree) in which every node has at most two children. A rooted tree naturally imparts a notion of levels (distance from the root), thus for every node a notion of children may be defined as the nodes connected to it a level below. Ordering of these children (e.g., by drawing them on a plane) makes it possible to distinguish a left child from a right child.  But this still doesn't distinguish between a node with left but not a right child from a one with right but no left child.

The necessary distinction can be made by first partitioning the edges, i.e., defining the binary tree as triplet (V, E1, E2), where (V, E1 ∪ E2) is a rooted tree (equivalently arborescence) and E1 ∩ E2 is empty, and also requiring that for all j ∈ { 1, 2 } every node has at most one Ej child.  A more informal way of making the distinction is to say, quoting the Encyclopedia of Mathematics, that "every node has a left child, a right child, neither, or both" and to specify that these "are all different" binary trees. 

## Types of binary trees

Tree terminology is not well-standardized and so varies in the literature.

• A rooted binary tree has a root node and every node has at most two children. A full binary tree An ancestry chart which maps to a perfect depth-4 binary tree.
• A full binary tree (sometimes referred to as a proper  or plane binary tree)   is a tree in which every node has either 0 or 2 children. Another way of defining a full binary tree is a recursive definition. A full binary tree is either: 
• A single vertex.
• A tree whose root node has two subtrees, both of which are full binary trees.
• In a complete binary tree every level, except possibly the last, is completely filled, and all nodes in the last level are as far left as possible. It can have between 1 and 2h nodes at the last level h.  An alternative definition is a perfect tree whose rightmost leaves (perhaps all) have been removed. Some authors use the term complete to refer instead to a perfect binary tree as defined below, in which case they call this type of tree (with a possibly not filled last level) an almost complete binary tree or nearly complete binary tree.   A complete binary tree can be efficiently represented using an array. A complete binary tree (that is not full)
• A perfect binary tree is a binary tree in which all interior nodes have two children and all leaves have the same depth or same level.  An example of a perfect binary tree is the (non-incestuous) ancestry chart of a person to a given depth, as each person has exactly two biological parents (one mother and one father). Provided the ancestry chart always displays the mother and the father on the same side for a given node, their sex can be seen as an analogy of left and right children, children being understood here as an algorithmic term. A perfect tree is therefore always complete but a complete tree is not necessarily perfect.
• In the infinite complete binary tree, every node has two children (and so the set of levels is countably infinite). The set of all nodes is countably infinite, but the set of all infinite paths from the root is uncountable, having the cardinality of the continuum. That's because these paths correspond by an order-preserving bijection to the points of the Cantor set, or (using the example of a Stern–Brocot tree) to the set of positive irrational numbers.
• A balanced binary tree is a binary tree structure in which the left and right subtrees of every node differ in height by no more than 1.  One may also consider binary trees where no leaf is much farther away from the root than any other leaf. (Different balancing schemes allow different definitions of "much farther".  )
• A degenerate (or pathological) tree is where each parent node has only one associated child node.  This means that the tree will behave like a linked list data structure.

## Properties of binary trees

• The number of nodes $n$ in a full binary tree, is at least $n=2h+1$ and at most $n=2^{h+1}-1$ , where $h$ is the height of the tree. A tree consisting of only a root node has a height of 0.
• The number of leaf nodes $l$ in a perfect binary tree, is $l=(n+1)/2$ because the number of non-leaf (a.k.a. internal) nodes $n-l=\sum _{k=0}^{\log _{2}(l)-1}2^{k}=2^{\log _{2}(l)}-1=l-1$ .
• This means that a full binary tree with $l$ leaves has $n=2l-1$ nodes.
• In a balanced full binary tree, $h=\lceil \log _{2}(l)\rceil +1=\lceil \log _{2}((n+1)/2)\rceil +1=\lceil \log _{2}(n+1)\rceil$ (see ceiling function).[ citation needed ]
• In a perfect full binary tree, $l=2^{h}$ thus $n=2^{h+1}-1$ .
• The number of null links (i.e., absent children of the nodes) in a binary tree of n nodes is (n+1).
• The number of internal nodes in a complete binary tree of n nodes is $\lfloor n/2\rfloor$ .
• For any non-empty binary tree with n0 leaf nodes and n2 nodes of degree 2, n0 = n2 + 1. 

## Combinatorics

In combinatorics one considers the problem of counting the number of full binary trees of a given size. Here the trees have no values attached to their nodes (this would just multiply the number of possible trees by an easily determined factor), and trees are distinguished only by their structure; however, the left and right child of any node are distinguished (if they are different trees, then interchanging them will produce a tree distinct from the original one). The size of the tree is taken to be the number n of internal nodes (those with two children); the other nodes are leaf nodes and there are n + 1 of them. The number of such binary trees of size n is equal to the number of ways of fully parenthesizing a string of n + 1 symbols (representing leaves) separated by n binary operators (representing internal nodes), to determine the argument subexpressions of each operator. For instance for n = 3 one has to parenthesize a string like $X*X*X*X$ , which is possible in five ways:

$((X*X)*X)*X,\qquad (X*(X*X))*X,\qquad (X*X)*(X*X),\qquad X*((X*X)*X),\qquad X*(X*(X*X)).$ The correspondence to binary trees should be obvious, and the addition of redundant parentheses (around an already parenthesized expression or around the full expression) is disallowed (or at least not counted as producing a new possibility).

There is a unique binary tree of size 0 (consisting of a single leaf), and any other binary tree is characterized by the pair of its left and right children; if these have sizes i and j respectively, the full tree has size i + j + 1. Therefore, the number $C_{n}$ of binary trees of size n has the following recursive description $C_{0}=1$ , and $\textstyle C_{n}=\sum _{i=0}^{n-1}C_{i}C_{n-1-i}$ for any positive integer n. It follows that $C_{n}$ is the Catalan number of index n.

The above parenthesized strings should not be confused with the set of words of length 2n in the Dyck language, which consist only of parentheses in such a way that they are properly balanced. The number of such strings satisfies the same recursive description (each Dyck word of length 2n is determined by the Dyck subword enclosed by the initial '(' and its matching ')' together with the Dyck subword remaining after that closing parenthesis, whose lengths 2i and 2j satisfy i + j + 1 = n); this number is therefore also the Catalan number $C_{n}$ . So there are also five Dyck words of length 6:

()()(),     ()(()),     (())(),     (()()),     ((()))

These Dyck words do not correspond to binary trees in the same way. Instead, they are related by the following recursively defined bijection: the Dyck word equal to the empty string corresponds to the binary tree of size 0 with only one leaf. Any other Dyck word can be written as ($w_{1}$ )$w_{2}$ , where $w_{1}$ ,$w_{2}$ are themselves (possibly empty) Dyck words and where the two written parentheses are matched. The bijection is then defined by letting the words $w_{1}$ and $w_{2}$ correspond to the binary trees that are the left and right children of the root.

A bijective correspondence can also be defined as follows: enclose the Dyck word in an extra pair of parentheses, so that the result can be interpreted as a Lisp list expression (with the empty list () as only occurring atom); then the dotted-pair expression for that proper list is a fully parenthesized expression (with NIL as symbol and '.' as operator) describing the corresponding binary tree (which is, in fact, the internal representation of the proper list).

The ability to represent binary trees as strings of symbols and parentheses implies that binary trees can represent the elements of a free magma on a singleton set.

## Methods for storing binary trees

Binary trees can be constructed from programming language primitives in several ways.

### Nodes and references

In a language with records and references, binary trees are typically constructed by having a tree node structure which contains some data and references to its left child and its right child. Sometimes it also contains a reference to its unique parent. If a node has fewer than two children, some of the child pointers may be set to a special null value, or to a special sentinel node.

This method of storing binary trees wastes a fair bit of memory, as the pointers will be null (or point to the sentinel) more than half the time; a more conservative representation alternative is threaded binary tree. 

In languages with tagged unions such as ML, a tree node is often a tagged union of two types of nodes, one of which is a 3-tuple of data, left child, and right child, and the other of which is a "leaf" node, which contains no data and functions much like the null value in a language with pointers. For example, the following line of code in OCaml (an ML dialect) defines a binary tree that stores a character in each node. 

typechr_tree=Empty|Nodeofchar*chr_tree*chr_tree

### Arrays

Binary trees can also be stored in breadth-first order as an implicit data structure in arrays, and if the tree is a complete binary tree, this method wastes no space. In this compact arrangement, if a node has an index i, its children are found at indices $2i+1$ (for the left child) and $2i+2$ (for the right), while its parent (if any) is found at index $\left\lfloor {\frac {i-1}{2}}\right\rfloor$ (assuming the root has index zero). Alternatively, with a 1-indexed array, the implementation is simplified with children found at $2i$ and $2i+1$ , and parent found at $\lfloor i/2\rfloor$ .  This method benefits from more compact storage and better locality of reference, particularly during a preorder traversal. However, it is expensive to grow[ citation needed ] and wastes space proportional[ citation needed ] to 2h - n for a tree of depth h with n nodes.

This method of storage is often used for binary heaps.[ citation needed ]

## Encodings

### Succinct encodings

A succinct data structure is one which occupies close to minimum possible space, as established by information theoretical lower bounds. The number of different binary trees on $n$ nodes is $\mathrm {C} _{n}$ , the $n$ th Catalan number (assuming we view trees with identical structure as identical). For large $n$ , this is about $4^{n}$ ; thus we need at least about $\log _{2}4^{n}=2n$ bits to encode it. A succinct binary tree therefore would occupy $2n+o(n)$ bits.

One simple representation which meets this bound is to visit the nodes of the tree in preorder, outputting "1" for an internal node and "0" for a leaf. If the tree contains data, we can simply simultaneously store it in a consecutive array in preorder. This function accomplishes this:

function EncodeSuccinct(node n, bitstring structure, array data) {     if n = nilthen         append 0 to structure;     else         append 1 to structure;         append n.data to data;         EncodeSuccinct(n.left, structure, data);         EncodeSuccinct(n.right, structure, data); }

The string structure has only $2n+1$ bits in the end, where $n$ is the number of (internal) nodes; we don't even have to store its length. To show that no information is lost, we can convert the output back to the original tree like this:

function DecodeSuccinct(bitstring structure, array data) {     remove first bit of structure and put it in bif b = 1 then         create a new node n         remove first element of data and put it in n.data         n.left = DecodeSuccinct(structure, data)         n.right = DecodeSuccinct(structure, data)         return n     elsereturn nil }

More sophisticated succinct representations allow not only compact storage of trees but even useful operations on those trees directly while they're still in their succinct form.

### Encoding general trees as binary trees

There is a one-to-one mapping between general ordered trees and binary trees, which in particular is used by Lisp to represent general ordered trees as binary trees. To convert a general ordered tree to a binary tree, we only need to represent the general tree in left-child right-sibling way. The result of this representation will automatically be a binary tree if viewed from a different perspective. Each node N in the ordered tree corresponds to a node N' in the binary tree; the left child of N' is the node corresponding to the first child of N, and the right child of N' is the node corresponding to N 's next sibling --- that is, the next node in order among the children of the parent of N. This binary tree representation of a general order tree is sometimes also referred to as a left-child right-sibling binary tree (also known as LCRS tree, doubly chained tree, filial-heir chain).

One way of thinking about this is that each node's children are in a linked list, chained together with their right fields, and the node only has a pointer to the beginning or head of this list, through its left field.

For example, in the tree on the left, A has the 6 children {B,C,D,E,F,G}. It can be converted into the binary tree on the right.

The binary tree can be thought of as the original tree tilted sideways, with the black left edges representing first child and the blue right edges representing next sibling. The leaves of the tree on the left would be written in Lisp as:

(((N O) I J) C D ((P) (Q)) F (M))

which would be implemented in memory as the binary tree on the right, without any letters on those nodes that have a left child.

## Common operations Tree rotations are very common internal operations on self-balancing binary trees.

There are a variety of different operations that can be performed on binary trees. Some are mutator operations, while others simply return useful information about the tree.

### Insertion

Nodes can be inserted into binary trees in between two other nodes or added after a leaf node. In binary trees, a node that is inserted is specified as to whose child it will be.

#### Leaf nodes

To add a new node after leaf node A, A assigns the new node as one of its children and the new node assigns node A as its parent.

#### Internal nodes The process of inserting a node into a binary tree

Insertion on internal nodes is slightly more complex than on leaf nodes. Say that the internal node is node A and that node B is the child of A. (If the insertion is to insert a right child, then B is the right child of A, and similarly with a left child insertion.) A assigns its child to the new node and the new node assigns its parent to A. Then the new node assigns its child to B and B assigns its parent as the new node.

### Deletion

Deletion is the process whereby a node is removed from the tree. Only certain nodes in a binary tree can be removed unambiguously. 

#### Node with zero or one children The process of deleting an internal node in a binary tree

Suppose that the node to delete is node A. If A has no children, deletion is accomplished by setting the child of A's parent to null. If A has one child, set the parent of A's child to A's parent and set the child of A's parent to A's child.

#### Node with two children

In a binary tree, a node with two children cannot be deleted unambiguously.  However, in certain binary trees (including binary search trees) these nodes can be deleted, though with a rearrangement of the tree structure.

### Traversal

Pre-order, in-order, and post-order traversal visit each node in a tree by recursively visiting each node in the left and right subtrees of the root.

#### Depth-first order

In depth-first order, we always attempt to visit the node farthest from the root node that we can, but with the caveat that it must be a child of a node we have already visited. Unlike a depth-first search on graphs, there is no need to remember all the nodes we have visited, because a tree cannot contain cycles. Pre-order is a special case of this. See depth-first search for more information.

#### Breadth-first order

Contrasting with depth-first order is breadth-first order, which always attempts to visit the node closest to the root that it has not already visited. See breadth-first search for more information. Also called a level-order traversal.

In a complete binary tree, a node's breadth-index (i − (2d − 1)) can be used as traversal instructions from the root. Reading bitwise from left to right, starting at bit d − 1, where d is the node's distance from the root (d = ⌊log2(i+1)⌋) and the node in question is not the root itself (d > 0). When the breadth-index is masked at bit d − 1, the bit values 0 and 1 mean to step either left or right, respectively. The process continues by successively checking the next bit to the right until there are no more. The rightmost bit indicates the final traversal from the desired node's parent to the node itself. There is a time-space trade-off between iterating a complete binary tree this way versus each node having pointer/s to its sibling/s.

## Related Research Articles In computer science, an AVL tree is a self-balancing binary search tree. It was the first such data structure to be invented. In an AVL tree, the heights of the two child subtrees of any node differ by at most one; if at any time they differ by more than one, rebalancing is done to restore this property. Lookup, insertion, and deletion all take O(log n) time in both the average and worst cases, where is the number of nodes in the tree prior to the operation. Insertions and deletions may require the tree to be rebalanced by one or more tree rotations. In computer science, a binary search tree (BST), also called an ordered or sorted binary tree, is a rooted binary tree whose internal nodes each store a key greater than all the keys in the node's left subtree and less than those in its right subtree. A binary tree is a type of data structure for storing data such as numbers in an organized way. Binary search trees allow binary search for fast lookup, addition and removal of data items, and can be used to implement dynamic sets and lookup tables. The order of nodes in a BST means that each comparison skips about half of the remaining tree, so the whole lookup takes time proportional to the binary logarithm of the number of items stored in the tree. This is much better than the linear time required to find items by key in an (unsorted) array, but slower than the corresponding operations on hash tables. Several variants of the binary search tree have been studied.

In computer science, a B-tree is a self-balancing tree data structure that maintains sorted data and allows searches, sequential access, insertions, and deletions in logarithmic time. The B-tree generalizes the binary search tree, allowing for nodes with more than two children. Unlike other self-balancing binary search trees, the B-tree is well suited for storage systems that read and write relatively large blocks of data, such as disks. It is commonly used in databases and file systems. In computer science and information theory, a Huffman code is a particular type of optimal prefix code that is commonly used for lossless data compression. The process of finding or using such a code proceeds by means of Huffman coding, an algorithm developed by David A. Huffman while he was a Sc.D. student at MIT, and published in the 1952 paper "A Method for the Construction of Minimum-Redundancy Codes".

In computer science, a red–black tree is a kind of self-balancing binary search tree. Each node stores an extra bit representing "color", used to ensure that the tree remains balanced during insertions and deletions. In computer science, a tree is a widely used abstract data type that simulates a hierarchical tree structure, with a root value and subtrees of children with a parent node, represented as a set of linked nodes. A binary heap is a heap data structure that takes the form of a binary tree. Binary heaps are a common way of implementing priority queues. The binary heap was introduced by J. W. J. Williams in 1964, as a data structure for heapsort.

In computer science, the treap and the randomized binary search tree are two closely related forms of binary search tree data structures that maintain a dynamic set of ordered keys and allow binary searches among the keys. After any sequence of insertions and deletions of keys, the shape of the tree is a random variable with the same probability distribution as a random binary tree; in particular, with high probability its height is proportional to the logarithm of the number of keys, so that each search, insertion, or deletion operation takes logarithmic time to perform.

In computer science, a search tree is a tree data structure used for locating specific keys from within a set. In order for a tree to function as a search tree, the key for each node must be greater than any keys in subtrees on the left, and less than any keys in subtrees on the right. A B+ tree is an m-ary tree with a variable but often large number of children per node. A B+ tree consists of a root, internal nodes and leaves. The root may be either a leaf or a node with two or more children.

In computer science, corecursion is a type of operation that is dual to recursion. Whereas recursion works analytically, starting on data further from a base case and breaking it down into smaller data and repeating until one reaches a base case, corecursion works synthetically, starting from a base case and building it up, iteratively producing data further removed from a base case. Put simply, corecursive algorithms use the data that they themselves produce, bit by bit, as they become available, and needed, to produce further bits of data. A similar but distinct concept is generative recursion which may lack a definite "direction" inherent in corecursion and recursion.

In computer science, a scapegoat tree is a self-balancing binary search tree, invented by Arne Andersson and again by Igal Galperin and Ronald L. Rivest. It provides worst-case O(log n) lookup time, and O(log n) amortized insertion and deletion time.

In computer science, an interval tree is a tree data structure to hold intervals. Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point. It is often used for windowing queries, for instance, to find all roads on a computerized map inside a rectangular viewport, or to find all visible elements inside a three-dimensional scene. A similar data structure is the segment tree. In computer science, a k-d tree is a space-partitioning data structure for organizing points in a k-dimensional space. k-d trees are a useful data structure for several applications, such as searches involving a multidimensional search key and creating point clouds. k-d trees are a special case of binary space partitioning trees. In graph theory, an m-ary tree is a rooted tree in which each node has no more than m children. A binary tree is the special case where m = 2, and a ternary tree is another case with m = 3 that limits its children to three.

In computer science, a leftist tree or leftist heap is a priority queue implemented with a variant of a binary heap. Every node x has an s-value which is the distance to the nearest leaf in subtree rooted at x. In contrast to a binary heap, a leftist tree attempts to be very unbalanced. In addition to the heap property, leftist trees are maintained so the right descendant of each node has the lower s-value.

A skew heap is a heap data structure implemented as a binary tree. Skew heaps are advantageous because of their ability to merge more quickly than binary heaps. In contrast with binary heaps, there are no structural constraints, so there is no guarantee that the height of the tree is logarithmic. Only two conditions must be satisfied:

In computer science, weight-balanced binary trees (WBTs) are a type of self-balancing binary search trees that can be used to implement dynamic sets, dictionaries (maps) and sequences. These trees were introduced by Nievergelt and Reingold in the 1970s as trees of bounded balance, or BB[α] trees. Their more common name is due to Knuth.

In computer science, a ball tree, balltree or metric tree, is a space partitioning data structure for organizing points in a multi-dimensional space. The ball tree gets its name from the fact that it partitions data points into a nested set of hyperspheres known as "balls". The resulting data structure has characteristics that make it useful for a number of applications, most notably nearest neighbor search. The Wavelet Tree is a succinct data structure to store strings in compressed space. It generalizes the and operations defined on bitvectors to arbitrary alphabets.

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