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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.1 Introduction to Algorithms 6.046J/18.401J/SMA5503 Lecture 9 Prof. Charles E. Leiserson

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.2 Binary-search-tree sort Create an empty BST for i = 1 to n do T REE -I NSERT (T, A[i]) Perform an inorder tree walk of T.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.3 Analysis of BST sort BST sort performs the same comparisons as quicksort, but in a different order! The expected time to build the tree is asymptotically the same as the running time of quicksort.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.4 Node depth The depth of a node = the number of comparisons made during TREE-INSERT. Assuming all input permutations are equally likely, we have Average node depth

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.5 Expected tree height But, average node depth of a randomly built BST = O(lg n) does not necessarily mean that its expected height is also O(lg n) (although it is).

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.6 Height of a randomly built binary search tree Outline of the analysis: Prove Jensens 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 Yn = 2Xn, where Xn is the random variable denoting the height of the BST. Prove that 2E[Xn] E[2Xn ] = E[Yn] = O(n3), and hence that E[Xn] = O(lg n).

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.7 Convex functions A function f : R R is convex if for all α,β 0 such that α + β = 1, we have f(αx + βy) αf(x) + βf(y)

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.8 Convexity lemma Lemma. Let f : R R be a convex function, and let {α1, α2, …, αn} be a set of nonnegative constants such that Σk αk = 1. Then, for any set {x1, x2, …, xn} of real numbers, we have Proof. By induction on n. For n = 1, we have α1 = 1, and hence f(α1x1) α1f(x1) trivially.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.9 Proof (continued) Inductive step: Algebra.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.10 Proof (continued) Inductive step: Convexity.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.11 Proof (continued) Inductive step: Convexity.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.12 Proof (continued) Inductive step:

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.13 Lemma. Let f be a convex function, and let X be a random variable. Then, f (E[X]) E[ f (X)]. Definition of expectation. Jensens inequality

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.14 Lemma. Let f be a convex function, and let X be a random variable. Then, f (E[X]) E[ f (X)]. Jensens inequality Convexity lemma (generalized).

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.15 Lemma. Let f be a convex function, and let X be a random variable. Then, f (E[X]) E[ f (X)]. Jensens inequality Tricky step, but truethink about it.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.16 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 of the root are randomly built. Hence, we have Y n = 2· max{Y k–1, Y n–k }.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.17 Analysis (continued) Define the indicator random variable Z nk as 1 if the root has rank k, 0 otherwise. Thus, Pr{Z nk = 1} = E[Z nk ] = 1/n, and

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.18 Exponential height recurrence Take expectation of both sides.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.19 Exponential height recurrence Linearity of expectation.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.20 Exponential height recurrence Independence of the rank of the root from the ranks of subtree roots.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.21 Exponential height recurrence The max of two nonnegative numbers is at most their sum, and E[Z nk ] = 1/n.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.22 Exponential height recurrence Each term appears twice, and reindex.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.23 Solving the recurrence Use substitution to show that E[Y n ] cn 3 for some positive constant c, which we can pick sufficiently large to handle the initial conditions.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.24 Solving the recurrence Use substitution to show that E[Y n ] cn 3 for some positive constant c, which we can pick sufficiently large to handle the initial conditions. Substitution.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.25 Solving the recurrence Use substitution to show that E[Y n ] cn 3 for some positive constant c, which we can pick sufficiently large to handle the initial conditions. Integral method.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.26 Solving the recurrence Use substitution to show that E[Y n ] cn 3 for some positive constant c, which we can pick sufficiently large to handle the initial conditions. Solve the integral.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.27 Algebra. Solving the recurrence Use substitution to show that E[Y n ] cn 3 for some positive constant c, which we can pick sufficiently large to handle the initial conditions.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.28 Putting it all together, we have 2 E[Xn] E[2 Xn ] Jensens inequality, since f(x) = 2 x is convex. The grand finale

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.29 The grand finale Putting it all together, we have Definition.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.30 The grand finale Putting it all together, we have What we just showed.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.31 The grand finale Putting it all together, we have Taking the lg of both sides yields

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.32 Post mortem Q. Does the analysis have to be this hard? Q. Why bother with analyzing exponential height? Q. Why not just develop the recurrence on directly?

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.33 Post mortem (continued) A. 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 max{2 a, 2 b } 2 a + 2 b allows the RHS to approach the LHS far more quickly as |a – b| increases. By using the convexity of f(x) = 2 x via Jensens inequality, we can manipulate the sum of exponentials, resulting in a tight analysis.

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© 2001 by Charles E. Leiserson Introduction to AlgorithmsDay 17 L9.34 Thought exercises See what happens when you try to do the analysis on X n directly. Try to understand better why the proof uses an exponential. Will a quadratic do? See if you can find a simpler argument. (This argument is a little simpler than the one in the bookI hope its correct!)

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