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## PowerPoint Slideshow about ' CS 253: Algorithms' - nellie

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How Fast Can We Sort?

- Insertion sort: O(n2)
- Bubble Sort, Selection Sort: (n2)
- Merge sort: (nlgn)
- Quicksort: (nlgn) - average
- What is common to all these algorithms?
- They all sort by making comparisons between the input elements

Comparison Sorts

- Comparison sorts use comparisons between elements to gain information about an input sequence a1, a2, …, an
- Perform tests:
ai < aj, ai≤aj, ai = aj, ai≥aj, orai>aj

to determine the relative order of aiandaj

- For simplicity, assume that all the elements are distinct

Lower-Bound for Sorting

Theorem:

To sort n elements, comparison sorts must make (nlgn)comparisons in the worst case.

leaf:

Decision Tree Model- Represents the comparisons made by a sorting algorithm on an input of a given size.
- Models all possible execution traces
- Control, data movement, other operations are ignored
- Count only the comparisons

Worst-case number of comparisons?

- Worst-case number of comparisons depends on:
- the length of the longest path from the root to a leaf
(i.e., the height of the decision tree)

- the length of the longest path from the root to a leaf

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LemmaAny binary tree of height hhas at most 2h leaves

Proof:by induction on h

Basis:h = 0 tree has one node, which is a leaf

# of Leaves = 1 ≤ 20 (TRUE)

Inductive step:assume true for h-1 (i.e. #Leaves ≤ 2h-1)

- Extend the height of the tree with one more level
- Each leaf becomes parent to two new leaves
No. of leaves at level h = 2 (no. of leaves at level h-1)

≤ 2 2h-1

≤ 2h

h-1

h

What is the least number of leaves in a Decision Tree Model?

- Allpermutations on n elements must appear as one of the leaves in the decision tree:
n! permutations

- At least n! leaves

Lower Bound for Comparison Sorts

Theorem:Any comparison sort algorithm requires

(nlgn)comparisons in the worst case.

Proof: How many leaves does the tree have?

- At least n! (each of the n!permutations must appear as a leaf)
- There are at most 2hleaves (by the previous Lemma)
n! ≤ 2h

h ≥ lg(n!) = (nlgn)

(see next slide)

h

leaves

Exercise 8.1-1: What is the smallest possible depth of a leaf in a decision tree for a comparison sort?

lg(n!) = (nlgn)

- n! ≤ nn lg(n!) ≤ nlgn lg(n!) = O(nlgn)
- n! ≥ 2n lg(n!) ≥ nlg2=n lg(n!) = Ω(n)
n ≤lg(n!) ≤ nlgn

- We need a tighter lower bound!
- UseStirling’s approximation (3.18):

Counting Sort

- Assumptions:
- Sort n integers which are in the range [0 ... r]
- r is in the order of n, that is, r=O(n)

- Idea:
- For each element x, find the number of elements ≤ x
- Place x into its correct position in the output array

( )

output array

Step 1

Find the number of times A[i] appears in A

Allocate C[1..r] (histogram)

For 1 ≤ i ≤ n, ++C[A[i]}

(i.e., frequencies/histogram)

Algorithm

- Start from the last element of A
- Place A[i] at its correct place in the output array
- Decrease C[A[i]] by one

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Example(cumulative sums)

(frequencies)

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Example (cont.)1

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Alg.: COUNTING-SORT(A, B, n, r)

- for i ← 0to r
- do C[ i ] ← 0
- for j ← 1to n
- do C[A[ j ]] ← C[A[ j ]] + 1
% C[i] contains the number of elements =i ; frequencies

- for i ← 1to r
- do C[ i ] ← C[ i ] + C[i -1]
% C[i] contains the number of elements ≤i ; cumulative sum

- for j ← ndownto1
- do B[C[A[ j ]]] ← A[ j ] % B[.] contains sorted array
- C[A[ j ]] ← C[A[ j ]] – 1

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Analysis of Counting Sort

Alg.: COUNTING-SORT(A, B, n, k)

- for i ← 0to r
- do C[ i ] ← 0
- for j ← 1to n
- do C[A[ j ]] ← C[A[ j ]] + 1
- for i ← 1to r
- do C[ i ] ← C[ i ] + C[i -1]
- for j ← ndownto1
- do B[C[A[ j ]]] ← A[ j ]
- C[A[ j ]] ← C[A[ j ]] – 1

(r)

(n)

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Overall time: (n + r)

Analysis of Counting Sort

- Overall time: (n + r)
- In practice we use COUNTING sort when r = O(n)
running time is (n)

- Counting sort is stable
- Counting sort is notin place sort

Radix Sort

- Represents keys as d-digit numbers in some base-k
e.g. key = x1x2...xdwhere 0 ≤ xi ≤ k-1

- Example: key=15
key10 = 15, d=2, k=10 where 0 ≤ xi ≤ 9

key2 = 1111, d=4, k=2 where 0 ≤ xi ≤ 1

Radix Sort

- 326
- 453
- 608
- 835
- 751
- 435
- 704
- 690

- Assumptions: d=Θ(1) and k =O(n)
- Sorting looks at one column at a time
- For a d digit number, sort the least significant digit first
- Continue sorting on the next least significant digit,
until all digits have been sorted

- Requires only d passes through the list

Radix Sort

Alg.: RADIX-SORT(A, d)

for i ← 1 to d

do use a stable sort to sort array A on digit i

- 1 is the lowest order digit, d is the highest-order digit

How do things go wrong if an unstable sorting alg. is used?

Analysis of Radix Sort

- Given n numbers of d digits each, where each digit may take up to k possible values, RADIX-SORT correctly sorts the numbers in (d(n+k))
- One pass of sorting per digit takes (n+k)
assuming that we use counting sort

- There are d passes (for each digit) (d(n+k))
Since d=Θ(1) and k =O(n)

Therefore, Radix Sort runs in (n) time

- One pass of sorting per digit takes (n+k)

Conclusions

- In the worst case, any comparison sort will take at least nlgnto sort an array of n numbers
- We can achieve a O(n) running time for sorting if we can make certain assumptions on the input data:
- Counting sort: each of the n input elements is an integer in the range [0 ... r]and r=O(n)
- Radix sort:the elements in the input are integers represented with d digits in base-k, where d=Θ(1) and k =O(n)

Problem

You are given 5 distinct numbers to sort.

Describe an algorithm which sorts them using at most 6comparisons, or argue that no such algorithm exists.

Solution:

Total # of leaves in the comparison tree = 5!

If the height of the tree is h, then (total # of leaves ≤ 2h)

2h ≥ 5!

h ≥ log2(5!)

≥ log2120

h > 6

There is at least one input permutation which will require at least 7 comparisons to sort. Therefore, no such algorithm exists.

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