1. Introduction to Algorithms Jiafen Liu Sept. 2013

2. Today’s Tasks Binary Search Trees(BST) Analyzing height Informal Analysis Formal Proof –Convexity lemma –Jensen’s inequality –Exponential height

3. Binary Search Tree Binary search tree is an popular data structure, it supports several dynamic operations. –Search –Minimum –Maximum –Predecessor –Successor –Insert –Delete –Tree Walk

4. Data structure of binary search tree A BST tree can be represented by a linked data structure in which each node is an structure. –Key field –Satellite data –Pointers: left, right, and parent For any node x, the keys in the left subtree of x less than or equal to key[x], and the keys in the right subtree of x are bigger than key[x].

5. Different Shapes of BST 2 binary search trees contains the same 6 keys. –(a) A binary search tree with height 3. –(b) A binary search tree with height 5. –Which one is better? Balanced Tree and Unbalanced Tree, What’s the worst case of BST?

6. What’s the cost of basic operations? It takes Θ(n) time to walk an n-node binary search tree. Other basic operations on a binary search tree take time proportional to the height of the tree Θ(h).

7. Tree Walk If x is the root of an n-node subtree, then the call INORDER-TREE-WALK(x) takes Θ(n) time. We will prove by induction.

8. Proof of the theorem Proof: Let T(n) denote the time taken this function. T(1) is a constant. For n > 1, suppose that function is called on a node x whose left subtree has k nodes and whose right subtree has n - k - 1 nodes. T(n) = T(k) + T(n - k - 1) + d How to Solve it? –substitution method

9. Search TREE-SEARCH (x, k) 1 if x= NIL or k = key[x] 2 then return x 3 if k < key[x] 4 then return TREE-SEARCH(left[x], k) 5 else return TREE-SEARCH(right[x], k)

10. Minimum and maximum TREE-MINIMUM (x) 1 while left[x] ≠ NIL 2 do x ← left[x] 3 return x TREE-MAXIMUM(x) 1 while right[x] ≠ NIL 2 do x ← right[x] 3 return x

11. Successor and predecessor TREE-SUCCESSOR(x) 1 if right[x] ≠ NIL 2 then return TREE-MINIMUM (right[x]) 3 y ← p[x] 4 while y ≠ NIL and x = right[y] 5 do x ← y 6 y ← p[y] 7 return y if the right subtree of node x is empty and x has a successor y, then y is the lowest ancestor of x whose left child is also an ancestor of x.

12. Insertion TREE-INSERT(T, z) 1 y ← NIL 2 x ← root[T] 3 while x ≠ NIL 4 do y ← x 5 if key[z] < key[x] 6 then x ← left[x] 7 else x ← right[x] 8 p[z] ← y 9 if y = NIL 10 then root[T] ← z // Tree T was empty 11 else if key[z] < key[y] 12 then left[y] ← z 13 else right[y] ← z

13. Deletion

14. Which node is actually removed depends on how many children z has. (a)If z has no children, we just remove it. (b)If z has only one child, we splice out z. (c)If z has two children, we splice out its successor y, which has at most one child, and then replace z's key and satellite data with y's key and satellite data.

15. Sorting and BST if there is an array A, can we sort this array using binary search tree operations as a black box? –Build the binary search tree, and then traverse it in order.

16. Sorting and BST Example: A= [3 1 8 2 6 7 5], what we will get? What's the running time of the algorithm?

17. Cost of build a BST of n nodes Can anybody guess an answer? –Θ(nlgn) in most case –Θ(n 2 ) in the worst case Does this looks familiar and remind you of any algorithm we've seen before? –Quicksort Process of Quicksort

18. BST sort and Quicksort BST sort performs the same comparisons as Quicksort, but in a different order! So, the expected time to build the tree is asymptotically the same as the running time of Quicksort.

19. Informal Proof The depth of a node equals to the number of comparisons made during TREE-INSERT. Assuming all input permutations are equally likely, we have Average node depth() = (comparison times to insert node i) = Θ(nlgn) = Θ(lgn) The expected running time of quicksort on n elements is?

20. Expected tree height Average node depth of a randomly built BST = Θ(lgn). Does this necessarily means that its expected height is also Θ(lgn)?

21. Outline of Formal Proof Prove Jensen’s inequality, which says that f(E[X]) ≤ E[f(X)] for any convex function f and random variable X. Analyze the exponential height of a randomly built BST on n nodes, which is the random variable Y n = 2 Xn, where X n is the random variable denoting the height of the BST. Prove that 2 E[Xn] ≤ E[2 Xn ] = E[Y n ] = O(n 3 ), and hence that E[X n ] = O(lgn).

22. Convex functions A function f is convex if for all α, β≥0 such that α+β=1, we have f(αx+ βy) ≤αf(x)+βf(y) for all x,y ∈ R.

23. Convexity lemma Lemma. Let f be a convex function, and let α 1, α 2, …, α n be nonnegative real numbers such that. Then, for any real numbers x 1, x 2, …, x n, we have How to prove that?

24. Proof of convexity lemma Proof. By induction on n. For n=1, we have α 1 =1 and hence f(α 1 x 1 ) ≤α 1 f(x 1 ) trivially. Inductive step: why we introduce 1-α n ? By convexity By induction

25. Convexity lemma: infinite case Lemma. Let f be a convex function, and let α 1, α 2, … be nonnegative real numbers such that. Then, for any real numbers x 1, x 2, …, we have assuming that these summations exist. We will not prove that, but intuitively its true.

26. Jensen’s inequality Jensen’s inequality, which says that f(E[X]) ≤ E[f(X)] for any convex function f and random variable X. Proof. By definition of expectation By Convexity lemma (infinite case)

27. Analysis of BST height Let X n be the random variable denoting the height of a randomly built binary search tree on n nodes, and let Y n = 2 X n be its exponential height. If the root of the tree has rank k, then X n = 1 + max{X k–1,X n–k } since each of the left and right subtrees are randomly built. Hence, we have Y n = 2·max{Y k–1,Y n–k }.

28. Analysis (continued) Define the indicator random variable Z nk as Thus, Pr{Z nk = 1} = E[Z nk ] = 1/n, and Take expectation of both sides.

29. Analysis (continued) Linearity of expectation. Independence of the rank of the root from the ranks of subtree roots. The max of two nonnegative numbers is at most their sum, and E[Z nk ] = 1/n. Each term appears twice, and reindex.

30. Analysis (continued) How to solve this? –Substitution Method Guess E[Y n ] ≤ cn 3 for some positive constant c. Initial conditions: for n=1, E[Y 1 ]=2 X1 =2≤c, we can pick c sufficiently large to handle.

31. Analysis (continued) Substitution: How to compute the expression on right? –Integral method.

32. The grand finale By Jensen’s inequality, since f(x) = 2 x is convex, we have What we just showed Taking lg of both sides yields

33. Post mortem Why bother with analyzing exponential height? Why not just develop the recurrence on X n = 1 + max{X k–1,X n–k } directly?

34. Answer The inequality max{a,b} ≤ a+b provides a poor upper bound, since the RHS approaches the LHS slowly as |a–b| increases. The bound allows the RHS to approach the LHS far more quickly as |a–b| increases. By using the convexity of f(x) = 2 x via Jensen’s inequality, we can manipulate the sum of exponentials, resulting in a tight analysis.