# Lecture 6: Greedy Algorithms I - PowerPoint PPT Presentation

1 / 26
Title:

## Lecture 6: Greedy Algorithms I

Description:

### Lecture 6: Greedy Algorithms I Shang-Hua Teng Optimization Problems A problem that may have many feasible solutions. Each solution has a value In maximization problem ... – PowerPoint PPT presentation

Number of Views:132
Avg rating:3.0/5.0
Slides: 27
Provided by: BarryK151
Category:
Tags:
Transcript and Presenter's Notes

Title: Lecture 6: Greedy Algorithms I

1
Lecture 6Greedy Algorithms I
• Shang-Hua Teng

2
Optimization Problems
• A problem that may have many feasible solutions.
• Each solution has a value
• In maximization problem, we wish to find a
solution to maximize the value
• In the minimization problem, we wish to find a
solution to minimize the value

3
The Diet Problem
Minimize 30 x1 80 x2 20 x3 s.t. 30x1
10 x2 6 x3 ? 300 5x1
9x2 8x3 ? 50 1.5x1 2.5 x2
18 x3 ? 70 10x1
6 x3 ? 100
x1, x2, x3 ? 0
4
Data Compression
• Suppose we have 1000000000 (1G) character data
file that we wish to include in an email.
• Suppose file only contains 26 letters a,,z.
• Suppose each letter a in a,,z occurs with
frequency fa.
• Suppose we encode each letter by a binary code
• If we use a fixed length code, we need 5 bits for
each character
• The resulting message length is
• Can we do better?

5
Huffman Codes
• Most character code systems (ASCII, unicode) use
fixed length encoding
• If frequency data is available and there is a
wide variety of frequencies, variable length
encoding can save 20 to 90 space
• Which characters should we assign shorter codes
which characters will have longer codes?

6
Data Compression A Smaller Example
• Suppose the file only has 6 letters a,b,c,d,e,f
with frequencies
• Fixed length 3G3000000000 bits
• Variable length

Fixed length
Variable length
7
How to decode?
• At first it is not obvious how decoding will
happen, but this is possible if we use prefix
codes

8
Prefix Codes
• No encoding of a character can be the prefix of
the longer encoding of another character, for
example, we could not encode t as 01 and x as
01101 since 01 is a prefix of 01101
• By using a binary tree representation we will
generate prefix codes provided all letters are
leaves

9
Prefix codes
• A message can be decoded uniquely.
• Following the tree until it reaches to a leaf,
and then repeat!
• Draw a few more tree and produce the codes!!!

10
Some Properties
• Prefix codes allow easy decoding
• Given a 0, b 101, c 100, d 111, e 1101, f
1100
• Decode 001011101 going left to right, 001011101,
a01011101, aa1011101, aab1101, aabe
• An optimal code must be a full binary tree (a
tree where every internal node has two children)
• For C leaves there are C-1 internal nodes
• The number of bits to encode a file is
• where f(c) is the freq of c, dT(c) is the tree
depth of c, which corresponds to the code length
of c

11
Optimal Prefix Coding Problem
• Input Given a set of n letters (c1,, cn) with
frequencies (f1,, fn).
• Construct a full binary tree T to define a prefix
code that minimizes the average code length

12
Greedy Algorithms
• Many optimization problems can be solved using a
greedy approach
• The basic principle is that local optimal
decisions may may be used to build an optimal
solution
• But the greedy approach may not always lead to an
optimal solution overall for all problems
• The key is knowing which problems will work with
this approach and which will not
• We will study
• The problem of generating Huffman codes

13
Greedy algorithms
• A greedy algorithm always makes the choice that
looks best at the moment
• My everyday examples
• Driving in Los Angeles, NY, or Boston for that
matter
• Playing cards
• Invest on stocks
• Choose a university
• The hope a locally optimal choice will lead to a
globally optimal solution
• For some problems, it works
• Greedy algorithms tend to be easier to code

14
David Huffmans idea
• A Term paper at MIT
• Build the tree (code) bottom-up in a greedy
fashion

15
Building the Encoding Tree
16
Building the Encoding Tree
17
Building the Encoding Tree
18
Building the Encoding Tree
19
Building the Encoding Tree
20
The Algorithm
• An appropriate data structure is a binary
min-heap
• Rebuilding the heap is lg n and n-1 extractions
are made, so the complexity is O( n lg n )
• The encoding is NOT unique, other encoding may
work just as well, but none will work better

21
Correctness of Huffmans Algorithm
Since each swap does not increase the cost, the
resulting tree T is also an optimal tree
22
Lemma 16.2
• Without loss of generality, assume fa?fb and
fx?fy
• The cost difference between T and T is

B(T) ? B(T), but T is optimal, B(T) ? B(T)
? B(T) B(T)Therefore T is an optimal tree
in which x and y appear as sibling leaves of
maximum depth
23
Correctness of Huffmans Algorithm
• Observation B(T) B(T) fx fy ? B(T)
B(T)-fx-fy
• For each c ?C x, y ? dT(c) dT(c)?
fcdT(c) fcdT(c)
• dT(x) dT(y) dT(z) 1
• fxdT(x) fydT(y) (fx fy)(dT(z)
1) fzdT(z) (fx fy)

24
B(T) B(T)-fx-fy
25
Proof of Lemma 16.3
• Suppose that T does not represent an optimal
prefix code for C. Then there exists a tree T
such that B(T) lt B(T).
• Without loss of generality, by Lemma 16.2, T
has x and y as siblings. Let T be the tree T
with the common parent x and y replaced by a leaf
with frequency fz fx fy.
Then
• B(T) B(T) - fx fy lt B(T) fx
fy B(T)
• T is better than T ? contradiction to the
assumption that T is an optimal prefix code for
C

26
How Did I learn about Huffman code?
• I was taking Information Theory Class at USC from
Professor Irving Reed (Reed-Solomon code)
• I was TAing for Introduction to Algorithms
• I taught a lecture on Huffman Code for
Professor Miller
• I wrote a paper