CSCI H343 Data Structures Fall 2024

Lecture: Balanced Binary Search Trees

Overview:

Introduce the Segment Intersection Project. Demo the solution.

There is a standard algorithm for testing whether 2 segments intersect and its time complexity is O(1).

Given a set of n segments, are their any pairs that intersect?

Brute force: test all combinations O(n²)

A better algorithm: Line Sweep

Requires pre-processing to ensure that:

Line Sweep ALgorithm:

We’ll need fast membership testing, insert, remove, and next/previous.

Motivation for balanced BSTs

Recall that search time is O(h), where h is the height of the tree.

Definition of height

int compute_height(Node n) {
    if (n == null) {
        return -1;
    } else {
        int hl = compute_height(n.left);
        int hr = compute_height(n.right);
        return 1 + Math.max(hl, hr);
    }
}

Example tree with heights in brackets:

                 41[3]
               /       \
            20[2]       65[1]
           /    \        /
         11[0]  29[1] 50[0]
          /
        26[0]

The problem of unbalanced trees

                o
                 \
                  o
                   \
                    o
                     \
                      o
                       \
                        o
                         \
                          o
                           \
                            o

height = n

vs.

                      o
                     / \
                    /   \
                   o     o
                  / \   / \
                 o   o o   o

height = log(n)

Definition A tree is balanced if its height is O(log n) where n is the number of nodes in the tree.

Equivalently, the number of nodes is Ω(2ʰ) where h is the height.

AVL Trees (Adelson-Velskii and Landis, 1962)

Definition The AVL Invariant: the height of two child subtrees may only differ by 1, for every node in the tree.

Examples of trees that are AVL:

          o               o          o         o
         /               / \        / \       / \
        o               o   o      o   o     o   o
                                      /     /     \
                                     o     o       o

Examples of trees that are not AVL:

    o      o              o
   /        \            / \
  o          o          o   o
   \          \        / \
    o          o      o   o
                           \
                            o

Red-black trees are an alternative: AVL is faster on lookup than red-black trees but slower on insertion and removal because AVL is more rigidly balanced.

Does the AVL invariant ensure that the tree is balanced?

Let N(h) represent the minimum number of nodes in an AVL tree of height h. (The least-balanced possible scenario.)

N(h) = N(h-1) + N(h-2) + 1

We want to show that

h ≲ log₂ N(h)

To simplify, we have

N(h-2) + N(h-2) + 1 < N(h-1) + N(h-2) + 1 = N(h)
2·N(h-2) + 1  < N(h)
2·N(h-2)      < N(h)
2·2·N(h-4)    < N(h)
2·2·2·N(h-6)  < N(h)
...
2^(h/2)       < N(h)

Take the log of both sides

log₂ 2^(h/2) < log₂ N(h)
                              (log₂ Aᴮ = B log₂ A)
h/2 · log₂ 2 < log₂ N(h)
                              (log₂ 2 = 1, i.e. 2¹ = 2)
h/2 · 1      < log₂ N(h)
                              (multiply both side by 2) 
h            < 2 · log₂ N(h)

so we have

h ≲ log₂ N(h)

How can we maintain the AVL invariant during insert? (remove is similar)

  1. Do the normal BST insert.

  2. Fix the AVL property if needed.

    We may need to fix problems along the entire path from the point of insertion on up to the root.

Example insertion and rebalancing:

             41
           /    \
         20      \
       /    \     65
      11     29  /
            /   50
          26

      insert(23) ==>

             41
           /    \
         20      \
       /    \     65
      11     29  /
            /   50
          26
         /
       23

Node 29 breaks the AVL invariant.

Tree Rotation

                y                         x
               / \    right_rotate(y)    / \
              x   C  --------------->   A   y
             / \     <-------------        / \
            A   B     left_rotate(x)      B   C

Rotations preserve the BST property and the in-order ordering.

A x B y C  =  A x B y C

Insert example: let’s use rotation to fix up our insert(23) example:

                   29
                  /    right_rotate(29)
                26     ---------------->    26
               /                           /  \
             23                          23    29

However, in different situations, the way in which we use tree rotation is different. So let’s look at more situations.

Insert example: insert(55)

              41
            /    \
          20      65
         /  \     /
        11   29  50
             /
           26

So 65 breaks the AVL invariant, and we have a zig-zag:

           65
          /
        50
          \
           55

A right rotation at 65 gives us a zag-zig, we’re not making progress!

          65(y)                        50(x)
         /        right_rotate(65)       \
        50(x)     ---------------->      65(y)
          \                              /
           55(B)                       55(B)

Instead, let’s try a left rotate at 50:

          65                           65
         /        left_rotate(50)     /
        50(x)     --------------->  55(y)
          \                         /
           55(y)                 50(x)

This looks familiar, now we can rotate right.

              65(y)                        55(x)
             /      right_rotate(65)       /  \
           55(x)    --------------->    50(A)  65(y)
          /
        50(A)

Another Insert Example

  _6[2]_
 /      \
3[0]     8[1]
         /  \
      7[0]   10[0]

Insert 11:

  _6[3]_
 /      \
3[0]     8[2]
         /  \
      7[0]   10[1]
                 \
                  11[0]

Student question

starting with an empty AVL tree, insert

14, 17, 11, 7, 4, 53, 13, 12, 8

Solution:

after insert 14, 17, 11, 7:

                   14
                  /  \
                11    17
               /
              7

insert 4:

                   14
                  /  \
                11    17
               /
              7
             /
            4

Node 11 doesn’t satisfy AVL.

rotate_right(11)

                14
               /  \
              7    17
             / \
            4   11

insert 54, 13, 12:

                   14
                  /  \
                 7    17
                / \     \
               4   11    54
                    \
                     13
                    /
                  12

Node 11 doesn’t satisfy AVL.

    rotate_right(13)

               11
                \
                 12
                   \
                   13

    rotate_left(11)

                   14
                  /  \
                 7    17
                / \    \
               4   12   54
                  /  \
                 11   13

insert 8:

                       14
                      /  \
                     7    17
                    / \    \
                   4   12   54
                      /  \
                     11   13
                    /
                   8

Node 7 doesn’t satisfy AVL. There’s a zig-zag.

    rotate_right(12)

                       14
                      /  \
                     7    17
                    / \    \
                   4   11   54
                      /  \
                     8    12
                           \
                           13

    left_rotate(7)

                       14
                      /  \
                     11   17
                    /  \    \
                   7   12   54
                  / \    \
                 4   8   13

Example: Remove Node and fix AVL

Given the following AVL Tree, delete the node with key 8. (This example has two nodes that end up violating the AVL property.)

         8
       /   \
      5     10
     / \   / \
    2   6 9   11
   / \   \      \
  1   3   7      12
       \
        4

Solution:

Algorithm for fixing AVL property

add or remove using the BST algorithm: O(log n)

number of iterations of the below “repeat”? O(log n) how much time per iteration (fix step): O(1) O(log n) * O(1) = O(log n)

total for add/remove and rebalance: O(log n) + O(log n) = O(log n)

Starting from the lowest changed node, repeat the following up to the root of the tree (because there can be several AVL violations).

Time Complexity of Insert and Remove for AVL Tree

O(h) = O(log n)

Using AVL Trees for sorting

ADT’s that can be implemented by AVL Trees