Description
Given a network, the connectivity problem is to determined if two given nodes in the network
are connected or not. An example of a network is shown on page 12 of the class handout,
lec23.pdf.
A general algorithm to solve the network connectivity problem is shown in Algorithm (2.3.1).
This algorithm is modified below to generate connected sets, which are embedded into the array
R such that R[v] is the node number of the root of the set. Once this is done, one can test the
connectivity of two nodes, i, j, by checking if R[i] = R[j ].
// Given G(V, E ) find connected vertex sets, generic version.
// Input: G(V, E )
// Output: Disjoint connected sets R[1 : n].
1 Algorithm Connectivity(G, R)
2 {
3 for each vi∈V do Si
:= {vi} ; // One element for each set.
4 NS := |V | ; // Number of disjoint sets.
5 for each e = (vi
, vj ) do { // Connected vertices
6 Si
:= SetFind(vi) ; Sj := SetFind(vj ) ;
7 if Si ̸= Sj then { // Unite two sets.
8 NS := NS − 1 ; // Number of disjoint sets decreases by 1.
9 SetUnion(Si
, Sj ) ;
10 }
11 }
12 for each vi ∈ V do { // Record root to R table.
13 R[i] := SetFind(vi) ;
14 }
15 }
For this homework, we are going to study the impacts of the disjoint set operations, SetFind
and SetUnion, to the performance. Specifically, three functions with different SetFind and
SetUnion are to be implemented.
1. void Connect1(void): This function uses the SetFind function listed in Algorithm (2.3.3)
and the SetUnion function as Algorithm (2.3.2) given in class handout.
2. void Connect2(void): This function replaces the SetUnion function by Algorithm (2.3.5),
WeightedUnion but keep the same SetFind function.
1
3. void Connect3(void): This function not only uses WeightedUnion but also replaces the
SetFind on line 6 by Algorithm (2.3.8) CollapsingFind. But, the SetFind function on
line 13 remains as it is.
Please also implement the main function as shown below to measure the performance. Note
that the graph is read in and stored as global variables such that each function can have efficient
access. The repetition number Nrepeat should be set to 100. At the end of program execution,
the CPU time and the number of disjoint sets for each function should be printed out.
// Driver function to measure 3 Connect functions.
// Input: network file contains G(V, E )
// Output: Disjoint connected sets R[1 : n].
1 Algorithm main()
2 {
3 readGraph() ; // Read a network from stdin.
4 t0 := GetTime() ; // Record time.
5 for i := 1 to Nrepeat do Connect1() ;
6 t1 := GetTime() ; Ns1 := NS ; // Record time and number of sets found.
7 for i := 1 to Nrepeat do Connect2() ;
8 t2 := GetTime() ; Ns2 := NS ; // Record time and number of sets found.
9 for i := 1 to Nrepeat do Connect3() ;
10 t3 := GetTime() ; Ns3 := NS ; // Record time and number of sets found.
11 write((t1 − t0)/Nrepeat, (t2 − t1)/Nrepeat, (t3 − t2)/Nrepeat, Ns1, Ns2, Ns3) ;
12 }
To test the performance of those functions, 10 sets of network files, g1.dat to g10.dat are
provided. The first line of each file contains two integers: |V |, the number of nodes, and |E|, the
number of edges in the network, followed by |E| lines, which contain two integers indicating the
two nodes connected by an edge. Using these 10 data files please compare the performance of
those three functions against your complexity analyses.
Example of program output is as follows:
$ a.out < g1.dat
|V| = 100, |E| = 143
Connect1 CPU time: 1.62411e-05, Disjoint sets: 1
Connect2 CPU time: 2.17915e-06, Disjoint sets: 1
Connect3 CPU time: 2.89917e-06, Disjoint sets: 1
2
Notes.
1. One executable and error-free C source file should be turned in. This source file should be
named as hw03.c.
2. A report file in pdf format is also needed. This file should be named as hw03a.pdf.
3. Submit your hw03.c and hw03a.pdf on EE workstations using the following command:
∼ee3980/bin/submit hw03 hw03.c hw03a.pdf
where hw03 indicates homework 3.
4. Your report should be clearly written such that I can understand it. The writing, including
English grammar, is part of the grading criteria.
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