Morioh
Login
Dylan Iqbal

Dylan Iqbal

2 years ago

JavaScript Algorithms and Data Structures: Binary Search Tree

In computer science, binary search trees (BST), sometimes called ordered or sorted binary trees, are a particular type of container: data structures that store "items" (such as numbers, names etc.) in memory. They allow fast lookup, addition and removal of items, and can be used to implement either dynamic sets of items, or lookup tables that allow finding an item by its key (e.g., finding the phone number of a person by name).

Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, on the basis of the comparison, to continue searching in the left or right subtrees. On average, this means that each comparison allows the operations to skip about half of the tree, so that each lookup, insertion or deletion takes time proportional to the 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.

A binary search tree of size 9 and depth 3, with 8 at the root. The leaves are not drawn.

Binary Search Tree

Pseudocode for Basic Operations

Insertion

insert(value)
  Pre: value has passed custom type checks for type T
  Post: value has been placed in the correct location in the tree
  if root = ø
    root ← node(value)
  else
    insertNode(root, value)
  end if
end insert

insertNode(current, value)
  Pre: current is the node to start from
  Post: value has been placed in the correct location in the tree
  if value < current.value
    if current.left = ø
      current.left ← node(value)
    else
      InsertNode(current.left, value)
    end if
  else
    if current.right = ø
      current.right ← node(value)
    else
      InsertNode(current.right, value)
    end if
  end if
end insertNode

Searching

contains(root, value)
  Pre: root is the root node of the tree, value is what we would like to locate
  Post: value is either located or not
  if root = ø
    return false
  end if
  if root.value = value
    return true
  else if value < root.value
    return contains(root.left, value)
  else
    return contains(root.right, value)
  end if
end contains

Deletion

remove(value)
  Pre: value is the value of the node to remove, root is the node of the BST
      count is the number of items in the BST
  Post: node with value is removed if found in which case yields true, otherwise false
  nodeToRemove ← findNode(value)
  if nodeToRemove = ø
    return false
  end if
  parent ← findParent(value)
  if count = 1
    root ← ø
  else if nodeToRemove.left = ø and nodeToRemove.right = ø
    if nodeToRemove.value < parent.value
      parent.left ←  nodeToRemove.right
    else
      parent.right ← nodeToRemove.right
    end if
  else if nodeToRemove.left != ø and nodeToRemove.right != ø
    next ← nodeToRemove.right
    while next.left != ø
      next ← next.left
    end while
    if next != nodeToRemove.right
      remove(next.value)
      nodeToRemove.value ← next.value
    else
      nodeToRemove.value ← next.value
      nodeToRemove.right ← nodeToRemove.right.right
    end if
  else
    if nodeToRemove.left = ø
      next ← nodeToRemove.right
    else
      next ← nodeToRemove.left
    end if
    if root = nodeToRemove
      root = next
    else if parent.left = nodeToRemove
      parent.left = next
    else if parent.right = nodeToRemove
      parent.right = next
    end if
  end if
  count ← count - 1
  return true
end remove

Find Parent of Node

findParent(value, root)
  Pre: value is the value of the node we want to find the parent of
       root is the root node of the BST and is != ø
  Post: a reference to the prent node of value if found; otherwise ø
  if value = root.value
    return ø
  end if
  if value < root.value
    if root.left = ø
      return ø
    else if root.left.value = value
      return root
    else
      return findParent(value, root.left)
    end if
  else
    if root.right = ø
      return ø
    else if root.right.value = value
      return root
    else
      return findParent(value, root.right)
    end if
  end if
end findParent

Find Node

findNode(root, value)
  Pre: value is the value of the node we want to find the parent of
       root is the root node of the BST
  Post: a reference to the node of value if found; otherwise ø
  if root = ø
    return ø
  end if
  if root.value = value
    return root
  else if value < root.value
    return findNode(root.left, value)
  else
    return findNode(root.right, value)
  end if
end findNode

Find Minimum

findMin(root)
  Pre: root is the root node of the BST
    root = ø
  Post: the smallest value in the BST is located
  if root.left = ø
    return root.value
  end if
  findMin(root.left)
end findMin

Find Maximum

findMax(root)
  Pre: root is the root node of the BST
    root = ø
  Post: the largest value in the BST is located
  if root.right = ø
    return root.value
  end if
  findMax(root.right)
end findMax

Traversal

InOrder Traversal

inorder(root)
  Pre: root is the root node of the BST
  Post: the nodes in the BST have been visited in inorder
  if root != ø
    inorder(root.left)
    yield root.value
    inorder(root.right)
  end if
end inorder

PreOrder Traversal

preorder(root)
  Pre: root is the root node of the BST
  Post: the nodes in the BST have been visited in preorder
  if root != ø
    yield root.value
    preorder(root.left)
    preorder(root.right)
  end if
end preorder

PostOrder Traversal

postorder(root)
  Pre: root is the root node of the BST
  Post: the nodes in the BST have been visited in postorder
  if root != ø
    postorder(root.left)
    postorder(root.right)
    yield root.value
  end if
end postorder

Complexities

Time Complexity

AccessSearchInsertionDeletion
O(log(n))O(log(n))O(log(n))O(log(n))

Space Complexity

O(n)

References

Read this in other languages: Português

The Original Article can be found on https://github.com

#javascript #algorithms #datastructures

 

JavaScript Algorithms and Data Structures: Binary Search Tree
4.70 GEEK