lecture 40 cse 331 dec 11, 2009. announcements solutions to hw 10 and graded hw 9 at end of the...
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Lecture 40
CSE 331Dec 11, 2009
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Announcements
Solutions to HW 10 and graded HW 9 at end of the lecture
Review session on Monday: see blog for details
Blog post on finals is up
Turn in your HW 10 by 1:10PM
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Finals
Noon – 2:30
NSC 210
Wed, Dec 16
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Longest vs Shortest Paths
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Two sides of the “same” coin
Shortest Path problem
Can be solved by a polynomial time algorithm
Is there a longest path of length n-1?
Given a path can verify in polynomial time if the answer is yes
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Poly time algo for longest path?
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P vs NP question
P: problems that can be solved by poly time algorithms
NP: problems that have polynomial time verifiable witness to optimal solution
Is P=NP?Is P=NP?
Alternate NP definition: Guess witness and verify!Alternate NP definition: Guess witness and verify!
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Proving P ≠ NP
Pick any one problem in NP and show it cannot be solved in poly time
Pretty much all known proof techniques
provably will not work
Pretty much all known proof techniques
provably will not work
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Proving P = NP
Will make cryptography collapse
Compute the encryption key!
Compute the encryption key!
Prove that all problems in NP can be solved by polynomial time algorithms
NP
NP-complete problems
NP-complete problems
Solving any ONE problem in here in
poly time will prove P=NP!
Solving any ONE problem in here in
poly time will prove P=NP!
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If you are curious for more
CSE431: Algorithms
CSE 396: Theory of Computation
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High level view of CSE 331Problem StatementProblem Statement
AlgorithmAlgorithm
Problem DefinitionProblem Definition
“Implementation”“Implementation”
AnalysisAnalysis Correctness+Runtime Analysis
Data Structures
Three general techniques
Three general techniques
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Now relax…
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Coding Theory
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The setupC(x)
x
y = C(x)+error
x Give up
Mapping C Error-correcting code or just code Encoding: x C(x) Decoding: y X C(x) is a codeword
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Different Channels and Codes• Internet
– Checksum used in multiple layers of TCP/IP stack
• Cell phones• Satellite broadcast
– TV• Deep space
telecommunications– Mars Rover
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“Unusual” Channels
• Data Storage– CDs and DVDs– RAID– ECC memory
• Paper bar codes– UPS (MaxiCode)
Codes are all around us
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Redundancy vs. Error-correction
• Repetition code: Repeat every bit say 100 times– Good error correcting properties– Too much redundancy
• Parity code: Add a parity bit– Minimum amount of redundancy– Bad error correcting properties• Two errors go completely undetected
• Neither of these codes are satisfactory
1 1 1 0 0 1
1 0 0 0 0 1
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Two main challenges in coding theory
• Problem with parity example– Messages mapped to codewords which do not
differ in many places• Need to pick a lot of codewords that differ a
lot from each other
• Efficient decoding– Naive algorithm: check received word with all
codewords
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The fundamental tradeoff
• Correct as many errors as possible with as little redundancy as possible
Can one achieve the “optimal” tradeoff with efficient encoding and decoding ?
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Datastream Algorithms
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Data Streams (one application)
• Databases are huge– Fully reside in disk memory
• Main memory– Fast, not much of it
• Disk memory– Slow, lots of it– Random access is expensive– Sequential scan is reasonably
cheap
Main memory
Disk Memory
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Data Streams (one application)• Given a restriction on number
of random accesses to disk memory
• How much main memory is required ?
• For computations such as join of tables
Main memory
Disk memory
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Whatever your impression of the 331
IT WASIT WAS
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Hopefully it was fun!
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Thanks!