Introduction To Bioinformatics Tutorial 2. Local Alignment Tutorial 2.
COMP2230 Tutorial 2
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Transcript of COMP2230 Tutorial 2
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COMP2230 Tutorial 2
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Mathematical Induction
A tool for proving the truth of a statement for some integer “n”.“n” is usually a +ve integer from 1 to infinity.Why using M.I.?
Usual Procedure• Prove the statement is true for n=k0 (e.g. n=1, n=10,…)• Assume it is true for n• Prove it is true for (n+1)
e.g. Prove 1+2+3+…..+ n =n(n+1)/2……………..eqn.1
n=1,
l.h.s of eqn 1 = 1,
r.h.s of eqn 1 = 1(1+1)/2=1. (i.e. eqn. 1 is true for n=1)
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Mathenatical Induction II
Assume eqn. 1. is true for n
i.e. 1+2+…..+n = n(n+1)/2
For (n+1),
l.h.s. = 1+2+….+n +(n+1) = n(n+1)/2 +(n+1) =(n+1)(n+2)/2
r.h.s = (n+1)(n+2)/2=l.h.s.
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Mathematical Induction III
How does M.I. work?
We have proved: 1. The statement/equation is true for n=1
2. If it is true for n, then it is true for (n+1)
The Equation 1+2+3+…….+n = n(n+1)/2
As The Equation is true for n=1, The Equation is true for n=2
As The Equation is true for n=2, The Equation is true for n=3
…………
The Equation is true for any +ve integer n.
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Useful assumptions
•For simplifying analysis
•Do not affect the asymptotic results
•E.g (ceiling, flooring) Why ceiling and flooring?
ceiling(2.3)=3, flooring(2.3)=2, round(2.3)=2.
ceiling(2.7)=3, flooring(2.7)=2, round(2.7)=3.
• For asymptotic analysis, T(1) can be regarded as the smallest T(n).
•If there is a recurrence T(n)=2T(n/4)+n, then T(1), T(2), T(3) can be treated as the same constant.
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Computing Complexity
1.Direct Counting. (for simple algorithms, non-recurrent algorithms)
You can refer to Tutorial 1.
2.Subsitution method (for recurrent algorithms).
Guess the form of the solution
Use M.I. to prove your guess.
3. Recursion tree
4. Master Theorems
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If both algorithms can be used to solve a problem, which one is more efficient, in time?
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16
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Another Example
T(n)=Θ(1) =1for 1≤n ≤ 3T(n)=3T(n/4)+Θ (n2), for n4
We make a simplifying assumption: n is a power of 4.
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Without expansion: T(n)First expansion
Cn2
T(n/4)
T(n/4) T(n/4)Second expansion C(n/4)2
C(n/4)2 C(n/4)2
…………………………………..T(n/16)……………………………….C(n/42)2
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L0: Cn2
L1: 3C(n/4)2
L2: 32C(n/42)2
L3: 33C(n/43)2
…..
Lk: 3kC(n/4k)2
If (n/4k) = 1, k=log4n
Total:k
k
i
iCn 3)16/3(1
0
2
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nk
i
iCn 4log1
0
2 3)16/3(
3log1
0
2 4)16/3( nCnk
i
i
Ans: O(n2) , Can it be Θ(n2)?
……. (what should we do to do to get the answer below)
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Exercise
Use substitution method, andMaster method to verify the
above result.
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note
Big-O concepts can’t be used within M.I. process.
A physical function, (e.g. cn2) which is a member of Big-O should be defined before M.I.
At the end of M.I. if it comes up with
T(n) ≤ guess(n) – (some +ve lower order terms), just ignore the lower order terms.
If there is a +ve lower order term left during M.I., add a lower order term to the physical function, guess(n).