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Balanced Trees

Learn about the efficiency of balanced trees and the algorithms for searching and inserting items. Explore 2-3 trees and 2-3-4 trees and understand how they help maintain balance in the tree structure.

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Balanced Trees

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  1. Balanced Trees Ellen Walker CPSC 201 Data Structures Hiram College

  2. Search Tree Efficiency • The average time to search a binary tree is the average path length from root to leaf • In a tree with N nodes, this is… • Best case: log N (the tree is full) • Worst case: N (the tree has only one path) • Worst case tree examples • Items inserted in order • Items inserted in reverse order

  3. Keeping Trees Balanced • Change the insert algorithm to rebalance the tree • Change the delete algorithm to rebalance the tree • Many different approaches, we’ll look at one • RED-BLACK trees • Based on non-binary trees (2-3-4 trees)

  4. 2-3 Trees • Relax constraint that a node has 2 children • Allow 2-child nodes and 3-child nodes • With bigger nodes, tree is shorter & branchier • 2-node is just like before (one item, two children) • 3-node has two values and 3 children (left, middle, right) < x , y> <=x >x and <=y >y

  5. Why 2-3 tree • Faster searching? • Actually, no. 2-3 tree is about as fast as an “equally balanced” binary tree, because you sometimes have to make 2 comparisons to get past a 3-node • Easier to keep balanced? • Yes, definitely. • Insertion can split 3-nodes into 2-nodes, or promote 2-nodes to 3-nodes to keep tree approximately balanced!

  6. 3-Node and Equivalent 2-Nodes 20 10 10,20 20 10 L R L M R M R L M

  7. Inserting into 2-3 Tree • As for binary tree, start by searching for the item • If you don’t find it, and you stop at a 2-node, upgrade the 2-node to a 3-node. • If you don’t find it, and you stop at a 3-node, you can’t just add another value. So, replace the 3-node by 2 2-nodes and push the middle value up to the parent node • Repeat recursively until you upgrade a 2-node or create a new root. • When is a new root created?

  8. Why is this better? • Intuitively, you unbalance a binary tree when you add height to one path significantly more than other possible paths. • With the 2-3 insert algorithm, you can only add height to the tree when you create a new root, and this adds one unit of height to all paths simultaneously. • Hence, the average path length of the tree stays close to log N.

  9. Deleting from a 2-3 Tree • Like for a binary tree, we want to start our deletion at a leaf • First, swap the value to be deleted with its immediate successor in the tree (like binary search tree delete) • Next, delete the value from the node. • If the node still has a value, you’ve changed a 3-node into a 2-node; you’re done • If no value is left, find a value from sibling or parent

  10. Deletion Cases • If leaf has 2 items, remove one item (done) • If leaf has 1 item • If sibling has 2 items, redistribute items among sibling, parent, and leaf • If sibling has 1 item, slide an item down from the parent to the sibling (merge) • Recursively redistribute and merge up the tree until no change is needed, or root is reached. (If root becomes empty, replace by its child) • Fig. 11.42-11.47, p. 602-603

  11. Going Another Step • If 2-3 trees are good, why not make bigger nodes? • 2-3-4 trees have 3 kinds of nodes • Remember a node is described by the number of children. It contains one less value than children • So, a 4-node has 4 children and 3 values. • Names of children are left, middle-left, middle-right, and right

  12. 4-node is equivalent to 3 2-nodes • 4 node has 3 values e.g. <10,20,30> • A binary tree of those values would have the middle value (20) as the parent, and the outer values (10, 20) as the children • So every 4-node can be replaced by 3 2-nodes. • This leads naturally to a very nice insertion algorithm.

  13. Insert into 2-3-4 tree • Find the place for the item in the usual way. • On the way down the tree, if you see any 4-nodes, split them and pass the middle value up. • If the leaf is a 2-node or 3-node, add the item to the leaf. • If the leaf is a 4-node, split it into 2 2-nodes, passing the middle value up to the parent node, then insert the item into the appropriate leaf node. • There will be room, because 4-nodes were split on the way down!

  14. 2-3-4 Insert Example 6, 15, 25 2,4,5 10 18, 20 30 Insert 24, then 19

  15. Insert 24: Split root first 15 6 25 2,4,5 10 18, 20,24 30

  16. Insert 19, Split leaf (20 up) first 15 6 20, 25 24 30 2,4,5 10 18, 19

  17. 2-3-4 Algorithm is Simpler • All splits happen on the way down the tree • Therefore, there is always room in the leaf for the insertion • And there is always room in the parent for a node that has to move up (because if the parent were a 4-node, it would already have been split!)

  18. Deleting from a 2-3-4 Tree • Find the value to be deleted and swap with inorder successor. • On the way down the tree (both for value and successor), upgrade 2-nodes into 3-nodes or 4 nodes. This ensures that the deleted value will be in a 3-node or 4-node leaf • Remove the value from the leaf.

  19. Upgrade cases • 2-node whose next sibling is a 2-node • Combine sibling values and “divider” value from parent into a 4-node • By the algorithm, parent cannot be a 2-node unless it is the root; in this case, our new 4-node becomes the root • 2-node whose next sibling is a 3-node • Move this value up to parent, move divider value down, shift a value to sibling

  20. Red-Black Trees • Red-Black trees are binary trees • But each node has an additional piece of information (color) • Red nodes can be considered (with their parents) as 3-nodes or 4-nodes • There can never be 2 red nodes in a row!

  21. Advantages of Red-black trees • Binary tree search algorithm and traversals hold directly (ignore color) • 2-3-4 tree insert and delete algorithms keep tree balanced (consider color)

  22. Splitting a 4-node • A 4-node in a RB tree looks like a black node with two red children. • If you make it a red node with 2 black children, you have split the node (and passed the parent up). • If the parent is red, you have to split it too.

  23. Revising a 3-node • To avoid having two red children in a row, you might have to rotate as well as color change. • When the parent is red: • If the parent’s value is between the child’s and the grandparent’s, do a single rotation • If the child’s value is between the parent’s and the grandparent’s, do a double rotation

  24. Single Rotation 4 8 4 3 8 6 3 6

  25. Double Rotation 8 8 6 4 4 6 5 6 5 8 4 5

  26. Top Down Insertion Algorithm • Search the binary tree in the usual way for the insertion algorithm • If you pass a 4-node (black node with two red children) on the way down, split it • Insert the node as a red child, and use the split algorithm to adjust via rotation if the parent is red also. • Force the root to be black after every insertion.

  27. Insert 1, 2, 3 2 1 1 1 3 2 2 Left single rotation 3 Insert red leaf (2 consecutive red nodes!)

  28. Continued: Insert 4, 5 2 2 2 1 3 1 3 1 4 3 4 4 5 4-node (2 red children) split on the way down Root remains black 5 Single rotation to avoidconsecutive red nodes

  29. Continued, Insert 9, 6 2 2 2 1 4 1 4 1 4 3 3 5 5 3 6 9 9 5 9 4-node (3,4,5) split on the way down, 4 is now red (passed up) Double rotation 6

  30. Deletion Algorithm • Find the node to be deleted (NTBD) • On the way down, if you pass a 2-node upgrade it by borrowing from its neighbor and/or parent • If the node is not a leaf node, • Find its immediate successor, upgrading all 2-nodes • Swap value of leaf node with value of NTBD • Remove the current leaf node, which is now NTBD (because of swap, if it happened)

  31. Red-black “neighbor” of a node • Let X be a 2-node to be deleted • If X is its parent’s left child, X’s right neighbor can be found by: • Let S = parent’s right child. If S is black, it is the neighbor • Otherwise, S’s left child is the neighbor. • If X is parent’s left child, then X’s left neighbor is grandparent’s left child.

  32. Neighbor examples 2 Right neighbor of 1 is 3 Right neighbor of 3 is 5 Left neighbor of 5 is 3 Left neighbor of 3 is 1 1 4 3 5 9

  33. Upgrade a 2-node • Find the 2-node’s neighbor (right if any, otherwise left) • If neighbor is also a 2-node (2 black children) • Create a 4-node from neighbors and their parent. • If neighbors are actually siblings, this is a color swap. • Otherwise, it requires a rotation • If neighbor is a 3-node or 4-node (red child) • Move “inner value” from neighbor to parent, and “dividing value” from parent to 2-node. • This is a rotation

  34. Deletion Examples (Delete 1) 2 4 2 6 1 4 1 3 5 9 3 6 Make a 4-node from 1, sibling 3, and “divider value” 2. [Single rotation of 2,4,6] 5 9

  35. Delete 6 4 4 2 6 2 9 3 5 9 3 5 6 4 Upgrade 6 by color flip, swap with successor (9) 2 9 3 5

  36. Delete 4 2 2 2 1 5 1 5 1 4 3 3 6 6 3 6 4 9 9 5 9 No 2-nodes to upgrade, swap with successor (5)

  37. Delete 2 2 2 1 5 1 6 3 6 5 9 9 3 Find 2-node enroute to successor (3) Neighbor is 3-node (6,9) Shift to get (3,5) and 9 as children, 6 up to parent. Single rotation

  38. Delete 2 (cont’d) 3 3 1 6 1 6 5 5 9 9 2 Swap with successor Remove leaf

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