### C语言代写 | CSE 101 Algorithms and Abstract Data Types

CSE 101

Algorithms and Abstract Data Types

Programming Assignment 2

In this assignment you will create a calculator for performing matrix operations that exploits the (expected)

sparseness of its matrix operands. An 𝑛 × 𝑛 square matrix is said to be sparse if the number of non-zero

entries (abbreviated NNZ) is small compared to the total number of entries, 𝑛

2

. The result will be a C

program capable of performing fast matrix operations, even on very large matrices, provided they are

sparse.

Given 𝑛 × 𝑛 matrices A and B, their product 𝐶 = 𝐴 ⋅ 𝐵 is the 𝑛 × 𝑛 matrix whose 𝑖𝑗th

entry is given by

𝐶𝑖𝑗 = ∑ 𝐴𝑖𝑘

𝑛

𝑘=1 𝐵𝑘𝑗.

Thus the element in the i

th row and j

th column of C is the vector dot product of the i

th row of A with the j

th

column of B. If we consider addition and multiplication of real numbers to be our basic operations, then

the above formula can be computed in time Θ(𝑛

3

), which is impractical for matrix sizes n of more than a

few thousand. If it so happens that A and B are sparse, then a great many of these arithmetic operations

involve adding or multiplying by zero, hence are unnecessary.

The sum S, and difference D, of A and B are the 𝑛 × 𝑛 matrices having 𝑖𝑗th

entries:

𝑆𝑖𝑗 = 𝐴𝑖𝑗 + 𝐵𝑖𝑗 and 𝐷𝑖𝑗 = 𝐴𝑖𝑗 − 𝐵𝑖𝑗

The scalar product of a real number x with A is denoted 𝑥𝐴, and has 𝑖𝑗th

entry (𝑥𝐴)𝑖𝑗 = 𝑥 ⋅ 𝐴𝑖𝑗. The

transpose of A, denoted 𝐴

𝑇

, is the matrix whose 𝑖𝑗

𝑡ℎ

entry is the 𝑗𝑖

𝑡ℎ

entry of A: (𝐴

𝑇

)𝑖𝑗 = 𝐴𝑗𝑖. In other

words, the rows of A are the columns of 𝐴

𝑇

, and the columns of A are the rows of 𝐴

𝑇

. Each of these

operations can be computed in time Θ(𝑛

2

), and just as for multiplication, their cost can be improved upon

significantly when A and B are sparse.

As one would expect, the cost of a matrix operation depends heavily on the choice of data structure used

to represent the matrix operands. There are several ways to represent a square matrix with real entries. The

standard approach is to use a 2-dimensional 𝑛 × 𝑛 array of doubles. The advantage of this representation

is that all of the above matrix operations have a straight-forward implementation using nested loops. This

project will use a very different representation however. Here you will represent a matrix as a 1-dimensional

array of Lists. Each List will represent one row of the Matrix, but only the non-zero entries will be stored.

Therefore List elements must store, not just the matrix entries, but the column index in which those entries

reside. For example, the matrix below would have the following representation as an array of Lists.

𝑀 = [

1.0 0.0 2.0

3.0 0.0 0.0

0.0 4.0 5.0

] Array of Lists: [

1: (1, 1.0) (3, 2.0)

2: (1, 3.0)

3: (2, 4.0) (3, 5.0)

This method obviously results in a substantial space savings when the Matrix is sparse. In addition, the

standard matrix operations defined above can be performed more efficiently on sparse matrices. As you

will see though, the matrix operations are much more difficult to implement using this representation. The

trade-off then, is a gain in space and time efficiency for sparse matrices, at the expense of more complicated

algorithms for performing standard matrix operations. Designing these algorithms in terms of our List ADT

operations will constitute the majority of the work you do on this assignment.

It will be necessary to make some minor changes to your List ADT from pa1. First you must convert your

ADT from a List of ints to a List of generic pointers. This entails changing certain field types, declaration

statements, method parameters, and return types from int to void*. The structs referred to by these List

elements will be defined in the Matrix ADT specified below. Second, it will be necessary to eliminate the

List operations equals() and copy(). The problem with these functions is that the List no longer knows what

it is a list of, and therefore can only perform “shallow” versions of these operations, i.e. compare or copy

pointers, not what they point to. For the same reason, function printList() is no longer required (but you

may wish to include it for diagnostic purposes only). The (optional) function cat() may also be included.

All other List operations from pa1 will be retained.

A “typical loop” in a client of List (as previously illustrated on page 3 of the pa1 project description) might

now appear as

type x; // Here “type” is whatever data type the void

// pointers in List are pointing to, known by

// the client but not by List.

moveFront(L);

while( index(L)>=0 ){

// Get current element, cast as the right kind of

// pointer, follow the pointer, assign value to x.

x = *(type*)get(L);

// do something with x, then

moveNext(L);

}

Observe that the equals(), copy() and printList() operations can still be performed, but they must now be

done from the client level. Be sure to test this modified List ADT before you try to implement the Matrix

ADT defined below. A (very weak) test of the modified List ADT called ListClient.c will be posted under

the examples section of the class webpage.

File Formats

The top level client module for this project will be called Sparse.c. It will take two command line arguments

giving the names of the input and output files, respectively. The input file will begin with a single line

containing three integers n, a and b, separated by spaces. The second line will be blank, and the following

a lines will specify the non-zero entries of an 𝑛 × 𝑛 matrix A. Each of these lines will contain a space

separated list of three numbers: two integers and a double, giving the row, column, and value of the

corresponding matrix entry. After another blank line, will follow b lines specifying the non-zero entries of

an 𝑛 × 𝑛 matrix B. For example, the two matrices

𝐴 = [

1.0 2.0 3.0

4.0 5.0 6.0

7.0 8.0 9.0

] and 𝐵 = [

1.0 0.0 1.0

0.0 0.0 0.0

1.0 1.0 1.0

]

are encoded by the following input file:

3 9 5

1 1 1.0

1 2 2.0

1 3 3.0

2 1 4.0

2 2 5.0

2 3 6.0

3 1 7.0

3 2 8.0

3 3 9.0

1 1 1.0

1 3 1.0

3 1 1.0

3 2 1.0

3 3 1.0

Your program will read an input file as above, initialize and build the Array of Lists representation of the

matrices A and B, then calculate and print the following matrices to the output file: 𝐴, 𝐵, (1.5)𝐴, 𝐴 + 𝐵,

𝐴 + 𝐴, 𝐵 − 𝐴, 𝐴 − 𝐴, 𝐴

𝑇

, 𝐴𝐵 and 𝐵

2

. The output file format is illustrated by the following example, which

corresponds to the above input file.

A has 9 non-zero entries:

1: (1, 1.0) (2, 2.0) (3, 3.0)

2: (1, 4.0) (2, 5.0) (3, 6.0)

3: (1, 7.0) (2, 8.0) (3, 9.0)

B has 5 non-zero entries:

1: (1, 1.0) (3, 1.0)

3: (1, 1.0) (2, 1.0) (3, 1.0)

(1.5)*A =

1: (1, 1.5) (2, 3.0) (3, 4.5)

2: (1, 6.0) (2, 7.5) (3, 9.0)

3: (1, 10.5) (2, 12.0) (3, 13.5)

A+B =

1: (1, 2.0) (2, 2.0) (3, 4.0)

2: (1, 4.0) (2, 5.0) (3, 6.0)

3: (1, 8.0) (2, 9.0) (3, 10.0)

A+A =

1: (1, 2.0) (2, 4.0) (3, 6.0)

2: (1, 8.0) (2, 10.0) (3, 12.0)

3: (1, 14.0) (2, 16.0) (3, 18.0)

B-A =

1: (2, -2.0) (3, -2.0)

2: (1, -4.0) (2, -5.0) (3, -6.0)

3: (1, -6.0) (2, -7.0) (3, -8.0)

A-A =

Transpose(A) =

1: (1, 1.0) (2, 4.0) (3, 7.0)

2: (1, 2.0) (2, 5.0) (3, 8.0)

3: (1, 3.0) (2, 6.0) (3, 9.0)

A*B =

1: (1, 4.0) (2, 3.0) (3, 4.0)

2: (1, 10.0) (2, 6.0) (3, 10.0)

3: (1, 16.0) (2, 9.0) (3, 16.0)

B*B =

1: (1, 2.0) (2, 1.0) (3, 2.0)

3: (1, 2.0) (2, 1.0) (3, 2.0)

Notice that the rows are to be printed in column sorted order, and zero rows are skipped altogether. A zero

matrix cause no output to be printed, as seen by the matrix 𝐴 − 𝐴 above. Note that, unlike the output file,

the input file may give matrix entries in any order.

Matrix ADT Specifications

In addition to the main program Sparse.c and the altered List.c from pa1, you will implement a Matrix ADT

in a file called Matrix.c. This ADT will contain a private inner struct (similar to Node in your List ADT)

encapsulating the column and value information corresponding to a matrix entry. You can give this inner

struct any name you wish, but I will refer to it here as Entry. Thus Entry is a pointer to a struct called

EntryObj that has two fields storing types int and double respectively. Your Matrix ADT will represent a

matrix as an array of Lists of Entries. Since Entry is itself a pointer, it can be assigned to a field of type

void*. It is required that each List of Entries be maintained in column sorted order. Your Matrix ADT

will export the following operations.

// newMatrix()

// Returns a reference to a new nXn Matrix object in the zero state.

Matrix newMatrix(int n)

// freeMatrix()

// Frees heap memory associated with *pM, sets *pM to NULL.

void freeMatrix(Matrix* pM);

// Access functions

// size()

// Return the size of square Matrix M.

int size(Matrix M);

// NNZ()

// Return the number of non-zero elements in M.

int NNZ(Matrix M);

// equals()

// Return true (1) if matrices A and B are equal, false (0) otherwise.

int equals(Matrix A, Matrix B);

// Manipulation procedures

// makeZero()

// Re-sets M to the zero Matrix.

void makeZero(Matrix M);

// changeEntry()

// Changes the ith row, jth column of M to the value x.

// Pre: 1<=i<=size(M), 1<=j<=size(M)

void changeEntry(Matrix M, int i, int j, double x);

// Matrix Arithmetic operations

// copy()

// Returns a reference to a new Matrix object having the same entries as A.

Matrix copy(Matrix A);

// transpose()

// Returns a reference to a new Matrix object representing the transpose

// of A.

Matrix transpose(Matrix A);

// scalarMult()

// Returns a reference to a new Matrix object representing xA.

Matrix scalarMult(double x, Matrix A);

// sum()

// Returns a reference to a new Matrix object representing A+B.

// pre: size(A)==size(B)

Matrix sum(Matrix A, Matrix B);

// diff()

// Returns a reference to a new Matrix object representing A-B.

// pre: size(A)==size(B)

Matrix diff(Matrix A, Matrix B);

// product()

// Returns a reference to a new Matrix object representing AB

// pre: size(A)==size(B)

Matrix product(Matrix A, Matrix B);

// printMatrix()

// Prints a string representation of Matrix M to filestream out. Zero rows

// are not printed. Each non-zero is represented as one line consisting

// of the row number, followed by a colon, a space, then a space separated

// list of pairs “(col, val)” giving the column numbers and non-zero values

// in that row. The double val will be rounded to 1 decimal point.

void printMatrix(FILE* out, Matrix M);

It is required that your program perform these operations efficiently. Let 𝑛 be the number of rows in 𝐴, and

let 𝑎 and 𝑏 denote the number of non-zero entries in 𝐴 and 𝐵 respectively. Then the worst case run times

of the above functions should have the following asymptotic growth rates.

changeEntry(A, i, j, x): Θ(𝑎)

copy(A): Θ(𝑛 + 𝑎)

transpose(A): Θ(𝑛 + 𝑎)

scalarMult(x, A): Θ(𝑛 + 𝑎)

sum(A, B): Θ(𝑛 + 𝑎 + 𝑏)

diff(A, B): Θ(𝑛 + 𝑎 + 𝑏)

product(A, B): Θ(𝑛

2 + 𝑎 ⋅ 𝑏)

It will be helpful to include a private function in Matrix.c with signature

double vectorDot(List P, List Q)

that computes the vector dot product of two matrix rows represented by Lists P and Q. Use this function

together with function transpose() to help implement product(). Similar helper functions for the

operations sum() and diff() will also be useful, and are highly recommended.

What to Turn In

Your project will be structured in three files: Sparse.c, Matrix.c, and List.c (together with header files

Matrix.h and List.h). The main program Sparse.c, will handle the input and output files and is the client of

the Matrix ADT, which is itself a client of the modified List ADT. Note that Sparse is not itself a direct

client of List, since it need not call any List operations. You will also write separate client modules

ListTest.c and MatrixTest.c to test the List and Matrix ADTs in isolation. Students often ask what

should be the contents of these test files. In each case, include enough calls to ADT operations to convince

the grader that you did in fact test your List and Matrix ADT modules in isolation before using them in the

larger project. The best way to do this is to actually use them for this purpose. At minimum they should

call every public function in their respective ADT modules at least once. The webpage link Examples/pa2

will contain files MatrixClient.c and ListClient.c. These should be considered to be weak tests of your

Matrix and ADT modules respectively, and are not adquate for your testing purposes. A number of matched

pairs of input/output files will also be included, along with a Python script for creating random input files.

Also submit a README file and a Makefile that creates an executable file called Sparse. (A Makefile is

also included in Examples/pa2 which you may alter as you see fit.) Thus in all, nine files will be submitted.

Sparse.c

Matrix.c

Matrix.h

MatrixTest.c

List.c

List.h

ListTest.c

Makefile

README

Submit these to the assignment name pa2 by the due date. As always, start early and ask questions.

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