Vazirani Presentation
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Transcript of Vazirani Presentation
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8/9/2019 Vazirani Presentation
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Vijay Vazirani
Presented by: Geoff Hollinger
CS599, Spring 2011
Approximation Algorithms
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Fun with complexity classes
P: problems that are “easy” to find a solution
NP: problems that are “easy” to check “yes”
Co-NP: problems that are “easy” to check “no”
NP technically refers to decision problems (yes/no) Also used for optimization problems (NP-hard to determine
if opt < threshold)
Conjectured diagram
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Given an NP-hard optimization problem
Finding the optimal solution S* is hard Finding a sub-optimal solution might be easy:
Where f(I) > 1 is a function of the instance I:
f(I) is a real number = constant factor approximation
f(I) could also be log(I), or other functions
What is an approximation algorithm?
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Vertex cover
Given a graph G = (V,E), a matching M in G is a set of
pairwise non-adjacent edges; that is, no two edges share acommon vertex
A maximal matching is a matching M of a graph G with theproperty that if any edge not in M is added to M, it is nolonger a matching
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Lower bounding maximal matching for
vertex cover
Basically, any vertex cover has to pick at least one endpoint of
each matched edge, and the algorithm picks both.
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Once you have a guarantee:
Can the approximation guarantee be improved by betteranalysis?
Counter: find a tight example
Can a better algorithm be designed using the same lower
bounding scheme?
Counter: find the integrality gap (more later)
Is there another lower bounding method that leads to an
improved guarantee? Counter: show finding a better approximation is NP-hard
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A tight example
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Set Cover
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A tight example for greedy set cover
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Steiner Tree
Proof sketch: take a Steiner tree of cost OPT and show you can
construct a spanning tree from it within 2*OPT.
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TSP
Proof sketch: show that this can be used to determine if the
graph contains a Hamiltonian cycle. Requires edges that
violate the triangle inequality.
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LPs
andPrimal/
Dual
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Set Cover ILP formulation
LP relaxation
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Set Cover via rounding
f is the frequency of the most frequent element
Algorithm 14.1 (Set cover via LP-rounding)
Find an optimal solution to the LP-relaxation
Pick all sets S for which xs >= 1/f in this solution
Theorem 14.2: Algorithm 14.1 achieves an approximation
factor of f for the set cover problem.
Proof sketch: The rounding process increases xs by at most afactor of f . Therefore, the cost is at most f times the cost of
the fractional cover.
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Designing a primal/dual schema
Relax constraints on either the primal or the dual program:
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Set cover via primal dual schema
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Now everyone can appreciate my
favorite xkcd comic