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Let S be a finite set. A binary relation on S is simply a collection R of ordered pairs(x,y)S×S. . For instance, S might be a set of people, and each such pair (x,y)R might mean “ x knows y ”.

An equivalence relationis a binary relation which satisfies three properties:

  • Reflexivity: localid="1659006645990" (x,y)R for all XS
  • Symmetry: If (x,y)R then (y,x)R
  • Transitivity: if (x,y)R and (y,z)R then localid="1659006784500" (x,Z)R

For instance, the binary relation “has the same birthday as” is an equivalence relation, whereas “is the father of” is not, since it violates all three properties.

Show that an equivalence relation partition set S into disjoint groups S1,S2,,Sk (in other words, S=S1S2SkandSiSj=ϕforallij ) such that:

  • Any two members of a group are related, that is, (x,y)R for any localid="1659006702579" (x,y)Si, for any i .
  • Members of different groups are not related, that is, for all ij, for all localid="1659006762355" xSi andySi, we have (x,Z)R.

(Hint: Represent an equivalence relation by an undirected graph.)

Short Answer

Expert verified

It can be shown that equivalence relation partitions set S into disjoint groups by the connected and disconnected branches.

Step by step solution

01

Explain the Equivalence relation

A relation is said to be in equivalence only if the relation satisfies reflexive, symmetry, and transitive properties.

02

Show that equivalence relation partitions set into disjoint groups.

Consider a set S that has the partitions of an undirected graph. Consider any tow vertices x and y in the undirected graph.

In an undirected graph, the relation between the two vertices are equivalent to the binary equivalence(x,y)Rfor anyx,ySi, for any i .Each connected branch in the graph is the equivalence class.

Obviously, Each connected graph is disjoint and all vertices are connected and each connected branch is disconnected from each other.

Therefore, anequivalence relation can partition set S into disjoint groups S1,S2,,Sk..

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Most popular questions from this chapter

As in the previous problem, you are given a binary tree T=(V,E) with designated root node. In addition, there is an array x[.]with a value for each node in V Define a new array z[.]as follows: for each uV,

z[u]=the maximum of the x-values associated with u’s descendants.

Give a linear-time algorithm that calculates the entire z-array.

A bipartite graph is a graph G=(V,E)whose vertices can be partitioned into two sets (V=V1V2andV1V2=ϕ) such that there are no edges between vertices in the same set (for instance, if , then there is no edge between and ).

(a) Give a linear-time algorithm to determine whether an undirected graph is bipartite.

(b) There are many other ways to formulate this property. For instance, an undirected graph is bipartite if and only if it can be colored with just two colors. Prove the following formulation:

an undirected graph is bipartite if and only if it contains no cycles of odd length.

(c) At most how many colors are needed to color in an undirected graph with exactly one odd length?

Infinite paths.Let G=(V,E) be a directed graph with a designated “start vertex” sV,asetVGV, a set of “good” vertices, and a set VBV of “bad” vertices. An infinite trace of is an infinite sequence of vertices viV such that (1)v0=s, and (2) for all i0, (vi,vi+1)E. That is, p is an infinite path in G starting at vertex s. Since the setV of vertices is finite, every infinite trace of Gmust visit some vertices infinitely often.

  1. If p is an infinite trace, let Inf(p)V be the set of vertices that occur infinitely often in p. Show that Inf(p) is a subset of a strongly connected component of G.
  2. Describe an algorithm that determines if role="math" G has an infinite trace.
  3. Describe an algorithm that determines if G has an infinite trace that visits some good vertex in VG infinitely often.
  4. Describe an algorithm that determines if role="math" localid="1659627728759" G has an infinite trace that visits some good vertex in VG infinitely often, but visits no bad vertex in VB infinitely often.

Design a linear-time algorithm which, given an undirected graph G and a particular edge ein it, determines whetherGhas a cycle containing.

In the 2SAT problem, you are given a set of clauses, where each clause is the disjunction (OR) of two literals (a literal is a Boolean variable of or the negation of a Boolean variable). You are looking for a way to assign a valuetrueorfalseto each of the variables so that all clauses are satisfied- that is, there is at least one true literal in each clause. For example, here’s an instance of 2SAT:

x1x2¯x1¯x3¯x1x2x3¯x4x1¯x4

This instance has a satisfying assignment: set x1,x2,x3, and x4 totrue, false, false,andtrue,respectively.

  1. Are there other satisfying truth assignments of this 2SAT formula? If so, find them all.
  2. Give an instance of 2SAT with four variables, and with no satisfying assignment.

The purpose of this problem is to lead you to a way of solving 2SAT efficiently by reducing it to the problem of finding the strongly connected components of a directed graph. Given an instance l of 2SAT with n variables and m clauses, construct a directed graph GI=V,E as follows.

  • GIhas 2nnodes, one for each variable and its negation.
  • GIhas 2m edges: for each clause αβof l (where α,βare literals), G1has an edge from the negation of α to β, and one from the negation ofβ to α.

Note that the clause αβis equivalent to either of the implications α¯β or β¯α. In this sense, data-custom-editor="chemistry" GI records all implications in l .

(C). Carry out this construction for the instance of 2SAT given above, and for the instance you constructed in (b).

(d). Show that if GI has a strongly connected component containing both x and X¯ for some variable x , then l has no satisfying assignment.

(e). Now show the converse of (d): namely, that if none of GI’s strongly connected components contain both a literal and its negation, then the instance l must be satisfiable.(Hint: Assign values to the variables as follows: repeatedly pick a sink strongly connected component of GI. Assign valuetrueto all literals in the sink, assignfalseto their negations, and delete all of these. Show that this ends up discovering a satisfying assignment.)

(f). Conclude that there is a linear-time algorithm for solving 2SAT.

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