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### Transcript of MA 252: Data Structures and Algorithms · 2012. 1. 16. · • E.g., insertion sort, merge sort,...

• MA 252: Data Structures and Algorithms

Lecture 9http://www.iitg.ernet.in/psm/indexing_ma252/y12/index.htmlhttp://www.iitg.ernet.in/psm/indexing_ma252/y12/index.html

Partha Sarathi Mandal

Dept. of Mathematics, IIT Guwahati

• • We are lucky if the partitioning is balance i.e.,

(n/2, n/2).

• We are unlucky if the partitioning is imbalance

i.e., (1, n-1) or (n-1, 1).

i.e., (1, n-1) or (n-1, 1).

P. S. Mandal, IITG

• More intuition

• Suppose we alternate lucky, unlucky, lucky, unlucky, lucky, ….

• L(n)= 2U(n/2) + Θ(n) lucky

• U(n)= L(n –1) + Θ(n) unlucky• U(n)= L(n –1) + Θ(n) unlucky

Solving:

L(n) = 2(L(n/2 –1) + Θ(n/2)) + Θ(n)

= 2L(n/2 –1) + Θ(n)

= Θ(nlg n)

How can we make sure we are usually lucky ?

P. S. Mandal, IITG

• Randomized quicksort

• IDEA: Partition around a random element.

• Running time is independent of the input order.

• No assumptions need to be made about the • No assumptions need to be made about the input distribution.

• No specific input elicits the worst-case behavior.

• The worst case is determined only by the output of a random-number generator.

P. S. Mandal, IITG

• Pseudocode for

Randomized quicksort

P. S. Mandal, IITG

• Randomized quicksort analysis

• Let T(n) =the random variable for the running time of randomized quicksort on an input of size n, assuming random numbers are independent.

• For k= 0, 1, …, n–1, define the indicator random variable:variable:

• E[Xk] = 0.Pr{Xk=0} + 1.Pr{Xk=1} = Pr{Xk = 1} = 1/n, since all splits are equally likely, assuming elements are distinct.

P. S. Mandal, IITG

• Analysis (continued)

P. S. Mandal, IITG

• Calculating expectation

P. S. Mandal, IITG

• Calculating expectation

P. S. Mandal, IITG

• Calculating expectation

P. S. Mandal, IITG

• Calculating expectation

P. S. Mandal, IITG

• Calculating expectation

P. S. Mandal, IITG

• Exercise

(The k = 0, 1 terms can be absorbed in the Θ(n) )

Exercise: Exercise:

Prove: E[T(n)] ≤ anlg n for constant a > 0.

– Choose a large enough so that anlgn dominates

E[T(n)] for sufficiently small n ≥ 2.

Use fact:

P. S. Mandal, IITG

• Substitution method

P. S. Mandal, IITG

• Substitution method

P. S. Mandal, IITG

• Substitution method

P. S. Mandal, IITG

• Substitution method

P. S. Mandal, IITG

• Quicksort in practice

• Quicksort is a great general-purpose sorting

algorithm.

• Quicksort is typically over twice as fast as

merge sort.merge sort.

• Quicksort can benefit substantially from code

tuning.

• Quicksort behaves well even with caching and

virtual memory.

P. S. Mandal, IITG

• How fast can we sort?

• All the sorting algorithms we have seen so far are comparison sorts: only use comparisons to determine the relative order of elements.

• E.g., insertion sort, merge sort, quicksort, heapsort.heapsort.

• The best worst-case running time that we’ve seen for comparison sorting is O(nlg n).

Is O(nlg n) the best we can do ?

• Decision tree scan help us answer this question.

P. S. Mandal, IITG

• Decision-tree example

• Each internal node is labeled i:j for i, j ∈ {1, 2, …, n}.

• The left subtree shows subsequent comparisons if ai ≤ aj.

• The right subtree shows subsequent comparisons if ai ≥ aj.

P. S. Mandal, IITG

• Decision-tree example

• Each internal node is labeled i:j for i, j ∈ {1, 2, …, n}.

• The left subtree shows subsequent comparisons if ai ≤ aj.

• The right subtree shows subsequent comparisons if ai ≥ aj.

P. S. Mandal, IITG

• Decision-tree example

• Each internal node is labeled i:j for i, j ∈ {1, 2, …, n}.

• The left subtree shows subsequent comparisons if ai ≤ aj.

• The right subtree shows subsequent comparisons if ai ≥ aj.

P. S. Mandal, IITG

• Decision-tree example

• Each internal node is labeled i:j for i, j ∈ {1, 2, …, n}.

• The left subtree shows subsequent comparisons if ai ≤ aj.

• The right subtree shows subsequent comparisons if ai ≥ aj.

P. S. Mandal, IITG

• Decision-tree example

• Each leaf contains a permutation 〈π(1), π(2),…, π(n)〉

to indicate that the ordering aπ(1) ≤ aπ(2) ≤ … ≤ aπ(n) has

been established.

P. S. Mandal, IITG

• Decision-tree model

A decision tree can model the execution of any comparison sort:

• One tree for each input size n.

• View the algorithm as splitting whenever it compares two elements.compares two elements.

• The tree contains the comparisons along all possible instruction traces.

• The running time of the algorithm = the length of the path taken.

• Worst-case running time = height of tree.

P. S. Mandal, IITG

• Lower bound for decision-tree sorting

Theorem: Any decision tree that can sort nelements must have height Ω(nlg n).

Proof: The tree must contain ≥ n! leaves, since there are n! possible permutations. A height-h binary tree has ≤ 2h leaves. Thus, n! ≤ 2h.

tree has ≤ 2h leaves. Thus, n! ≤ 2h.

∴ h ≥ lg (n!) (lg is monotonically increasing)

≥ lg ((n/e)n) (Stirling’s formula)

= nlg n – nlg e

= Ω(nlg n)

P. S. Mandal, IITG

• Lower bound for comparison sorting

Corollary: Heapsort and merge sort are

asymptotically optimal comparison sorting

algorithms.

P. S. Mandal, IITG