A vertex cover of a graph G=(V,E)is a subset of vertices SVthat includes at least one endpoint of every edge in E. Give a linear-time algorithm for the following task.

Input: An undirected tree T=(V,E).

Output: The size of the smallest vertex cover of T. For instance, in the following tree, possible vertex covers include{A,B,C,D,E,F,G}and{A,C,D,F}but not{C,E,F}.The smallest vertex cover has size 3: {B,E,G}.

Short Answer

Expert verified

The recursion will run in linear time that is On.

Step by step solution

01

Defining Recurrence relation

We have given a graphG=V,E where we have to find the smallest vertex cover. Vertex cover is a set of vertices that includes at least one endpoint of every edge of the graph. In order to find the size of smallest vertex cover of the given graph, we will use dynamic programming approach where we will find out all possible vertex cover of all subproblems and then select that vertex cover which is smallest.

vi=minj:jChildi1+k:kChildjvk,1+j:jChildivj

02

Solution.

Let us suppose that for each node ‘(u)’ in the graph, we considerVuas the size of minimum vertex cover for a subtree which is rooted at node ‘u’.

Fig: A undirected graph.

Vertex cover is a set of vertices that includes at least one endpoint of every edge of the graph. In order to find the size of smallest vertex cover of the given graph, we will use dynamic programming approach where we will find out all possible vertex cover of all subproblems and then select that vertex cover which is smallest.

Our base condition will be when our node ‘u’ is leaf node. In this case, Vu=0. This is because we cannot obtain subtree from leaf node.

For any internal node of a subtree, we have:

vi=minj:jChildi1+k:kChildjvk,1+j:jChildivj

On solving the above recursive equation, we will get output as Vr, where r is the root of the tree. Thus,Vris the size of the minimum vertex cover. The algorithm according to the above stated recursion relation, solve all the subproblem in order of decreasing depth of the tree.

This recursion will run in linear time that is On.

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Most popular questions from this chapter

Is there a faster way to compute the nth Fibonacci number than by fib2 (page 4)? One idea involves matrices.

We start by writing the equations F1=F1 and F2=F0+F1 in matrix notation:


role="math" localid="1659767046297" (F1F2)=(0111).(F0F1).

Similarly,

F2F3=(0111).(F1F2)=(0111)2.(F0F1)

And in general

(FnFn+1)=(0111)n.(F0F1)

So, in order to compute Fn, it suffices to raise this 2×2 matrix, call it X, to the nth power.

a. Show that two 2×2matrices can be multiplied using 4additions and 8multiplications.

But how many matrix multiplications does it take to compute Xn?

b. Show that O(logn) matrix multiplications suffice for computing Xn. (Hint: Think about computing X8.)

Thus, the number of arithmetic operations needed by our matrix-based algorithm, call it fib3, is just O(logn), as compared to O(n)for fib2. Have we broken another exponential barrier? The catch is that our new algorithm involves multiplication, not just addition; and multiplications of large numbers are slower than additions. We have already seen that, when the complexity of arithmetic operations is taken into account, the running time offib2becomes O(n).

c. Show that all intermediate results of fib3 are O(n) bits long.


d. Let M(n)be the running time of an algorithm for multiplying n-bit numbers, and assume that M(n)=O(n2) (the school method for multiplication, recalled in Chapter 1, achieves this). Prove that the running time of fib3 is O(M(n)logn).


e. Can you prove that the running time of fib3 is O(M(n))? Assume M(n)=Θ(na)for some 1a2. (Hint: The lengths of the numbers being multiplied get doubled with every squaring.)


In conclusion, whether fib3 is faster than fib2 depends on whether we can multiply n-bit integers faster thanO(n2) . Do you think this is possible? (The answer is in Chapter 2.) Finally, there is a formula for the Fibonacci numbers:

role="math" localid="1659768125292" Fn=15(1+52)n15(152)n.

So, it would appear that we only need to raise a couple of numbers to the nth power in order to computeFn . The problem is that these numbers are irrational, and computing them to sufficient accuracy is nontrivial. In fact, our matrix method fib3 can be seen as a roundabout way of raising these irrational numbers to the nth power. If you know your linear algebra, you should see why. (Hint: What are the eigenvalues of the matrix X?)

Here’s a problem that occurs in automatic program analysis. For a set of variablesx1,......,xn, you are given some equality constraints, of the form “ xi=xj” and some disequality constraints, of the form “ xixj.” Is it possible to satisfy all of them?

For instance, the constraints.

x1=x2,x2=x3,x3=x4,x1x4

cannot be satisfied. Give an efficient algorithm that takes as input m constraints over n variables and decides whether the constraints can be satisfied.

Question: 0.1. In each of the following situations, indicate whether f=O(g),orf=Ω(g),or both (in which case f=(g))

How long does the recursive multiplication algorithm (page 25) take to multiply an n -bit number by an m -bit number? Justify your answer.

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