Numerical.methods.for.Scientists.and.Engineers 2ed Hamming 0486652416
ON THE EFFICIENCY OF THE HAMMING C-CENTERSTRING PROBLEMS Amihood Amir Liam Roditty Jessica Ficler...
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Transcript of ON THE EFFICIENCY OF THE HAMMING C-CENTERSTRING PROBLEMS Amihood Amir Liam Roditty Jessica Ficler...
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ON THE EFFICIENCY OF THE HAMMING C-CENTERSTRING PROBLEMS
Amihood AmirLiam RodittyJessica Ficler
Oren Sar Shalom
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Motivation – the Conference Location Problem
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Consensus String Problem
Output: Find a point whose maximum Distance from all points is smallest
Input: points in space.
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Hamming Distance
•
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Consensus String Problem (1-HRC)
•
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History:
Frances and Litman [1997]:Problem is NP-complete even for binary alphabets
Therefore: 3 directions.
1. Solution for small k.2. Fixed parameter tractability.3. Approximation algorithms.
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History:
Solution for small k:
Gramm, Niedermeier, and Rossmanith [2001] (3)
Boucher, Brown, and Durocher [2008] (4 binary)
A., Landau, Na, Park, Park, and Sim [2009] (3, radius & dist. sum optimization)
A., Paryenty, and Roditty [2012] (5 binary, l 2
for all k: l k)
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History:
Fixed Parameter Tractability for all Parameters:
Fixed l: Ben-Dor, Lancia, Perone, and Ravi [1997]
Fixed k: Gramm, Niedermeier, and Rossmanith [2003]
Fixed d: Sojanovic, Berman, Gumucio, Hardison, and Miller [1997] Lanctot, Li, Ma, Wang, and Zhang [1999] Sze, Lu, and Chen [2004]
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History:Approximations:
PTAS: Li, Ma, and Wang [2002] – not practical.
Rounded LP: Ben-Dor, Lancia, Perone, and Ravi [1997]
large number of variables: |Σ|l Chimani, Woste, and Bocker [2011]:
can be reduced to: |Σ|(l-1) A., Paryenty, and Roditty [2011]: |T(S)| |Σ| (T(S)= set of column types)
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Another Motivation – Clustering.The C-CenterStrings problemInput:1. Points in space2. Number c3. Objective function f.
Output: Divide the points to c sets such that for the c consensus strings c1,c2,…,cc, f(c1,c2,…,cc) is maximum/minimum.
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Three Types of Objective functions:• Let HRC (Hamming Radius Clustering) be the consensus
string problem defined before.
1.c-HRC: partition into c sets, each of which has center with radius d.
• 2. c-HRLC: partition into c sets, each of which has center with radius d, but center is part of input set.
• 3. c-HRSC: partition into c sets, each of which has a center and the sum of the radii does not exceed d.
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The Hamming radius c-clustering problem (c-HRC)Example:
For the following strings and d=1, we show it belongs to 2-HRC.
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The Hamming radius local c-clustering problem (c-HRLC)Example:
For the following strings and d=2, we show it belongs to 2-HRLC.
Does it belong to 2-HRLC when d=1 ?
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The Hamming radius c-clustering sum problem (c-HRSC)Example:
For the following strings and d=2, we show it belongs to 2-HRC.
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In this Paper:
We consider:
1. Parametetrized Complexity, and
2. Approximations
Small k is not too meaningful in the context of clustering.
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C-CenterString Parameterized Complexity
c Fixedk Fixed
d Fixed(d=1)
d/l and c Fixed
l Fixed(l=2)
HRC NPCpolynomial
time NPCpolynomial
time ?
HRLCpolynomial
timepolynomial
time ?polynomial
time NPC
HRSC NPCpolynomial
time ?polynomial
time ?
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Theorem: HRC,HRLC and HRSC can be solved in polynomial time for fixed k.
• If k≤c then input strings can be assigned to c centers where d=0.
• Otherwise c<k. There are ck<kk options for partitioning k strings to c sets.
- For each set, find the consensus center in
polynomial time.- The partition that gives the best result is
the optimal solution.
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C-CenterString Parameterized Complexity
c Fixedk Fixed
d Fixed(d=1)
d/l and c Fixed
l Fixed(l=2)
HRC NPCpolynomial
time NPCpolynomial
time ?
HRLCpolynomial
timepolynomial
time ?polynomial
time NPC
HRSC NPCpolynomial
time ?polynomial
time ?
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Theorem: HRC is NP complete even if the radius is fixed to d = 1.
• d = 1 and the alphabet is binary• By reduction from Vertex Cover For Triangle-Free Graphs
• Our input:• G - Triangle-Free Graph• t – size of vertex-cover set
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• The construction:
The c parameter is t.
The distance parameter d is 1.
1
4
2
3
5 6
7
1 2 3 4 5 6 7
1 0 0 1 0 0 0
0 1 1 0 0 0 0
0 1 0 1 0 0 0
1 0 1 0 0 0 0
0 0 1 0 1 0 0
0 0 0 1 0 1 0
0 0 0 0 1 0 1
0 0 0 0 0 1 1
Encode edges as bit strings of length |V|. Set the bits of the vertices on the two sides of the edge.
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•
1
4
2
3
5 6
7
0 1 1 0 0 0 0
1 0 1 0 0 0 0
0 0 1 0 1 0 0
0 0 1 0 0 0 0
0 0 0 1 0 0 0
1 0 0 1 0 0 0
0 1 0 1 0 0 0
0 0 0 1 0 1 0
0 0 0 0 0 0 1
0 0 0 0 1 0 1
0 0 0 0 0 1 1
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•
0 1 1 0 0 0 0
1 0 1 0 0 0 0
0 0 1 0 1 0 0
0 0 1 0 0 0 0
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•
0 0 0 ? ? ? ?
0 0 0 0 1 0 1
0 0 0 1 0 1 0
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•
0 1 1 0 0 0 0
1 0 1 0 0 0 0
1 1 0 0 0 0 0
1 2 3 4 5 6 71
32
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C-CenterString Parameterized Complexity
c Fixedk Fixed
d Fixed(d=1)
d/l and c Fixed
l Fixed(l=2)
HRC NPCpolynomial
time NPCpolynomial
time ?
HRLCpolynomial
timepolynomial
time ?polynomial
time NPC
HRSC NPCpolynomial
time ?polynomial
time ?
![Page 26: ON THE EFFICIENCY OF THE HAMMING C-CENTERSTRING PROBLEMS Amihood Amir Liam Roditty Jessica Ficler Oren Sar Shalom.](https://reader036.fdocuments.us/reader036/viewer/2022081520/5697bf8e1a28abf838c8c975/html5/thumbnails/26.jpg)
Theorem: HRLC is NP complete even if
the length is fixed to l=2• We prove by reduction from Minimum Maximal
Matching for Bipartite graphs• Our input:
• G – Bipartite Graph• t – size of the minimal set that is maximal matching
Maximal MatchingMinimumMaximal Matching
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• The construction:
The c parameter is t.
The distance parameter d is 1.
1
4
2
3
5
1 2
1 4
3 2
3 4
5 4
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•
1
4
2
3
5
1 2
3 2
3 4
3 2
1 4
5 4
5 4
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•
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•
3 2 5 4
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•
1 2 1 3
6 2
1 2
1 4
5 2
1 2
1 3
5 3
1 3
6 2
5 2
1 2
1 2
1 4
1 3
5 3
1 3
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6 2
5 2
1 2
1 2
1 4
1 3
5 3
1 3
Move strings [6,2] and [5,2] if there are centers begins in 5 or 6 5 2
1 2
1 2
1 4
1 3
5 3
1 3
6 2
6 7
6 7
Change the center to one of the remaining strings 5 2
5 2
1 2
1 4
1 3
5 3
1 3
6 2
6 7
6 7
We keep going until there are no two centers with common symbol !
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Approximation Algorithms
• 1. A linear-time 4-Approximation for the 2-HRSC problem.
• 2. A polynomial time 3-Approximation for the 2-HRSC problem.
• 3. Special case PTAS – by computing the clusters and doing 1-HRC approximation on each cluster.
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•
>2d>2d
>2d
Lemma
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Proof•
center
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• If we had a representative from each cluster we can associate the rest of the strings to the appropriate group
• Now use a knownapproximation algorithmof 1-HRC, for finding the consensus strings of each cluster
>2d
>2d
>2d
![Page 37: ON THE EFFICIENCY OF THE HAMMING C-CENTERSTRING PROBLEMS Amihood Amir Liam Roditty Jessica Ficler Oren Sar Shalom.](https://reader036.fdocuments.us/reader036/viewer/2022081520/5697bf8e1a28abf838c8c975/html5/thumbnails/37.jpg)
•
>4d
Lemma
Cluster c-center
Cluster c-center
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Proof
≤d
≤d
≤d
≤d
≤d
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•
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0
![Page 40: ON THE EFFICIENCY OF THE HAMMING C-CENTERSTRING PROBLEMS Amihood Amir Liam Roditty Jessica Ficler Oren Sar Shalom.](https://reader036.fdocuments.us/reader036/viewer/2022081520/5697bf8e1a28abf838c8c975/html5/thumbnails/40.jpg)
•
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0
1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0 0 0
![Page 41: ON THE EFFICIENCY OF THE HAMMING C-CENTERSTRING PROBLEMS Amihood Amir Liam Roditty Jessica Ficler Oren Sar Shalom.](https://reader036.fdocuments.us/reader036/viewer/2022081520/5697bf8e1a28abf838c8c975/html5/thumbnails/41.jpg)
Polynomial time approximation algorithm for 2-HRSC problem•
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•
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•
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•
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•
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Future work
1. We presented a heuristic algorithm that did very well in practice – what is its approximation ratio?
2. There are some gaps in the parameterized complexity table: a. What happens in the HRLC/HRSC cases for fixed d?
b. What happens in the HRC/HRSC cases for fixed l?
3. Is there a PTAS for c-HRC?
4. Can we approximate c-HRC using LP? SDP?