本次Java代写是完成数据结构中的最小生成树

CSC 226 SUMMER 2020

ASSIGNMENT 2 – PROGRAM

The assignment is to implement an algorithm to determine if the minimum weight spanning tree of an

edge-weighted, connected graph 𝐺, with no self-loops or parallel edges, is the same when using Prim’s

algorithm as it is when using Kruskal’s algorithm. The edge weights in 𝐺 will be real numbers greater

than 0 and will not necessarily be distinct. A Java template has been provided containing an empty

method PrimVsKruskal, which takes a single argument consisting of a weighted adjacency matrix for an

edge-weighted graph 𝐺 with real number edge weights all greater than 0. The expected behavior of the

method is as follows:

Input: An 𝑛 × 𝑛 array 𝐺, of type double, representing an edge-weighted graph.

Output: A boolean value which is true if the Prim’s MST equals the Kruskal’s MST and false

otherwise.

A correct implementation of the PrimVsKruskal class will find the minimum weight spanning tree using

the textbook’s implementation of Prim’s algorithm (eager version) and the minimum weight spanning

tree using the textbook’s implementation of Kruskal’s algorithm and compare. If they are the same it

returns true, otherwise false.

You must use the provided Java template as the basis of your submission, and put your implementation

inside the PrimVsKruskal method in the template. You may not change the name, return type or

parameters of the PrimVsKruskal method. You may add additional methods as needed. You may use any

of the classes provided by the textbook as your code will be run with the algs4.jar file. Most likely you

will use some or all of the following: UF class, IndexMinPQ class, MinPQ class, Edge class, Queue class,

etc. The main method in the template contains code to help you test your implementation by reading an

adjacency matrix from a file. You may modify the main method to help with testing, but only the

contents of the PrimVsKruskal method (and any methods you have added) will be marked, since the

main function will be deleted before marking begins. Please read through the comments in the template

file before starting.

I foresee three ways of solving this problem, each increasingly more difficult and worth increasingly

more marks. (1) The simplest way to solve this problem is to convert the adjacency matrix into an

EdgeWeightedGraph object, running PrimMST and KruskalMST on the graph and then comparing the

output trees to one another. (2) Another way to solve the problem is to leave the graph as an adjacency

matrix and modifying the code of PrimMST and KruskalMST (inside your PrimVsKruskal class) to work

directly with the adjacency matrix. You would still generate the two MSTs and compare them. (3) The

most difficult but most efficient way to solve it also involves working directly with the adjacency matrix

but here you build the two trees concurrently, testing the consistency of the trees after adding each edge.

We will call this an “early detection system”, which will potentially recognize if the two trees are

unequal before completion of the trees.

2

2 Input Format

The testing code in the main function of the template reads a graph in a weighted adjacency matrix

format and uses the PrimVsKruskal class to compare the two minimum spanning trees. A weighted

adjacency matrix 𝐴 for an edge-weighted graph 𝐺 on 𝑛 vertices is an 𝑛 × 𝑛 matrix where entry (𝑖,𝑗)

gives the weight of the edge between vertices 𝑖 and 𝑗 (or 0 if no edge exists). For example, the matrix

𝐴 =

[

0 1 2 0 0 0 0

1 0 3 4 4 0 0

2 3 0 0 0 0 6

0 4 0 0 4 4 0

0 4 0 4 0 5 0

0 0 0 4 5 0 7

0 0 6 0 0 7 0]

corresponds to the edge-weighted graph below. Note that the weighted adjacency matrix for an

undirected graph is always symmetric.

The input format used by the testing code in main consists of the number of vertices 𝑛 followed by the

𝑛 × 𝑛 weighted adjacency matrix. The graph above would be specified as follows:

7

0 1 2 0 0 0 0

1 0 3 4 4 0 0

2 3 0 0 0 0 6

0 4 0 0 4 4 0

0 4 0 4 0 5 0

0 0 0 4 5 0 7

0 0 6 0 0 7 0

3 Test Datasets

A collection of randomly generated edge-weighted graphs with positive, real number edge weights will

be used to test your code. I will provide some example files but you are encouraged to create your own

test inputs to ensure that your implementation functions correctly in all cases.

4

4

4

4

5

7

6

2 3

1

0 1

2

4

3 5

6

3

4 Sample Run

The output of a model solution on the graph above is given in the listing below.

Reading input values from test1.txt.

Does Prim MST = Kruskal MST? true

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